Machine Learning for Blockchain Data Analysis: Challenges and Opportunities

If you work with blockchain data, you know how quickly it has outgrown simple tools. New chains, heavy contract activity, and constant asset movement create streams of information that are hard to organise or interpret by hand.

This raises an important question to the front. Can machine learning for blockchain data analysis bring clarity to this volume, or does it risk creating new blind spots?

The scale continues to rise. Blockchains processed over $10 trillion in on-chain transactions in 2024, and every team working on analytics, security, or compliance now depends on systems that can keep up with this growth.

This blog explains why blockchain data has become so complex, how machine learning is being used to analyse it, and the limitations you should plan for. 

Key Takeaways

• ​​Machine learning (ML) can greatly enhance blockchain data analysis by identifying fraud, improving security, and predicting risks.
• Key challenges include limited labelled datasets, privacy constraints, and the computational cost of real-time data training.
• Blockchain data’s complexity is compounded by multi-chain inconsistency, adversarial activities, and the need for privacy-preserving techniques like zero-knowledge proofs.
• ML is already being applied to real-world blockchain projects like fraud detection, wallet analysis, and smart contract auditing.
• Despite current limitations, emerging opportunities for ML in blockchain include predictive protocol security, on-chain identity scoring, and privacy-preserving analytics.

Why Is Blockchain Data Becoming Harder to Analyse?

Blockchain data is becoming harder to analyse because it now spans multiple chains, layers, and transaction types, all of which combine to create complexity at a scale few teams are fully equipped to handle.

Before diving deeper into where machine learning can help, it helps to understand the specific dimensions of the data problem:

1. Multi-chain activity and data fragmentation

More than 1,000 blockchains are operating globally. Each one has its own structure, consensus model, and data layer, which means analytics tools must handle a wide variety of formats and sources.

2. Proliferation of Layer-2s, sidechains, and modular architectures

Layer-2 solutions continue to grow, with ecosystems built on rollups, app-chains, and sidechains. These add additional data streams for optimisation and settlement beyond legacy Layer-1 chains.

3. Explosion of DeFi, MEV, cross-chain bridges, and zero-knowledge interactivity

Complex transaction types, such as bridges, rollups, liquidation cascades, and MEV exploitation, generate nested, time-ordered structures that typical analytics platforms were never built to interpret.

4. Traditional analytics tools struggle with this scale and structure

According to a recent study, blockchain data analytics faces persistent issues with data accessibility, scalability, accuracy, and interoperability thatconventional modelscannot resolve. 

Standard dashboards cannot fully normalise disparate chains, detect pseudonymous network entities, or model evolving transaction graphs.

Also Read: Building an AI App: Steps and Timeline 

How Machine Learning Is Changing Blockchain Intelligence

Machine learning is being used in blockchain intelligence to apply advanced algorithms to identify patterns, predict risks, and highlight anomalies that traditional analytics miss. 

Machine learning models analyse blockchain data in real time, providing insights that enhance security, efficiency, and issue detection in blockchain systems.

Some key ML applications include: 

1. Detecting anomalies in transaction graphs

ML models can identify abnormal transaction patterns that indicate fraud, rug pulls, or wash trades. These malicious activities are often hidden within high-frequency transactions or across multiple addresses. A study found that over $1.9 billion in cryptocurrency was lost to scams in 2022. Machine learning models can flag these fraudulent activities faster and with more accuracy than rule-based systems.

2. Predicting liquidation risk in DeFi

ML can forecast when DeFi positions are likely to be liquidated, using historical data, market volatility, and collateral health. By analysing past liquidation events, ML models predict future risk and help platforms trigger early alerts, reducing user losses. 

For instance, platforms like Aave and Compound now use predictive analytics to alert users of imminent liquidation risks based on market trends.

3. Wallet clustering without explicit identity data

ML techniques like clustering and graph analysis can group wallets belonging to the same user, even without explicit identity information. This helps identify whales, track token movements, and monitor suspicious activity across large wallets, improving transparency and security. 

A report from Elliptic shows that over $1.5 billion in illicit transactions were detected using wallet clustering in 2023. This demonstrates the power of ML in blockchain forensics.

4. Identifying insider trading patterns from on-chain and off-chain data

By combining on-chain transaction data with off-chain activities (such as social media sentiment or exchange order books), ML models can detect insider trading patterns in blockchain networks. This helps identify when traders gain unfair advantages based on undisclosed information. 

5. Forecasting network congestion or gas spikes

ML models predict network congestion and gas price spikes by analysing transaction volume, block space utilisation, and historical network data. This allows DeFi protocols and exchanges to optimise fees and transaction times. 

For example, Ethereum’s gas prices have fluctuated dramatically, and ML can now predict these fluctuations based on past congestion patterns, enabling users to time transactions more effectively.

6. Detecting protocol health issues from validator behaviour

Machine learning can track validator performance on proof-of-stake networks to identify potential issues that may lead to downtime, forks, or security breaches. 

By monitoring validator behaviours such as missed attestations, latency, and performance metrics, ML can predict network vulnerabilities before they impact users.

Ready to get the potential of blockchain for your business? At Codewave, we integrate blockchain with the latest technologies to drive transparency, security, and efficiency. Whether it’s developing smart contracts, building decentralized applications, or implementing private blockchain solutions, we deliver custom solutions designed to scale with your business. 

Before we dive into the opportunities, it’s important to address the real challenges holding back the widespread application of machine learning in blockchain.

What Problems Still Limit ML on Blockchain Data?

Machine learning promises to extract value from blockchain data, but many organisations hit roadblocks when they try to deploy models in live systems. 

These issues range from data quality to multisystem integration, and each one affects how well ML performs in blockchain analytics.

Key technical obstacles include:

Challenge	Description	Solution
Limited labelled datasets	Many blockchain datasets lack predefined labels needed for supervised ML.	Use unsupervised or semi‑supervised models, create synthetic labels, and invest in data pipelines.
Privacy constraints and zero‑knowledge demands	Data privacy, pseudonymity, and ZK‑proofs make access and feature design complex.	Combine federated learning and homomorphic encryption; adopt privacy‑by‑design frameworks.
Difficulty modelling large on‑chain graph data	Transaction graphs grow rapidly, are high-dimensional, and exhibit temporal complexity.	Use graph neural networks, incremental training techniques, and data summarisation tools.
Multi‑chain inconsistencies	Different chains use divergent data formats and transaction protocols, confusing ML features.	Develop unified data schemas, build chain‑agnostic feature extraction layers, and standardise inputs.
Adversarial activity targeting ML systems	Malicious actors exploit patterns to evade detection by ML models.	Regularly retrain models, use anomaly detection, and implement adversarial robustness testing.
High computational overhead for real‑time training	On‑chain data updates fast; training in real‑time demands high compute and memory resources.	Use stream processing and model distillation, and apply edge or cloud-hybrid training strategies.

Also Read: Why Cloud Computing Is Key to Digital Transformation

Despite the obstacles, machine learning continues to offer exciting opportunities to enhance blockchain analytics. 

Let’s explore some of the key areas where these advancements are already making an impact.

What New Opportunities Are Emerging for ML‑Driven Blockchain Analytics?

Machine learning for blockchaindata analysis is entering a phase where real, usable applications are taking shape. As research shows, the number of published studies on ML and blockchain hasgrown by nearly 50 % in the last year. 

This highlights a shift from concept to execution, organisations can now move beyond experimentation and build systems that deliver measurable value.

Here are six promising areas where ML is poised to enhance blockchain analytics:

• Cross‑chain execution pattern modelling: ML can process transactions across multiple chains to detect when assets move unexpectedly between protocols, enabling deeper insight into multi‑chain behaviour.
• Predictive modelling for protocol security risks: By analysing validator actions, staking trends and past attack data, ML models can flag protocols at higher risk of failure or compromise before issues arise.
• On‑chain identity scoring with privacy in mind: ML algorithms can assign trust scores to wallets using behavioural and transactional features, all without exposing personal identity or sensitive data.
• Smart contract code review accelerated by ML: Instead of relying solely on manual audits, ML tools analyse contract code for vulnerabilities, rank severity, and reduce audit time while increasing coverage.
• Early‑warning systems for bridges, collusion, and systemic threats: ML models monitor bridge flows, staking pools and unusual validator clusters to spot potential attack vectors such as collusion or exploit builds.
• Privacy‑preserving analytics via ML + zero‑knowledge technologies: Combining ML with ZK proofs enables analysis on encrypted or obfuscated datasets, unlocking insight without compromising user privacy or network integrity.

To truly understand the value machine learning brings to blockchain, let’s look at how leading projects are currently applying it and the results they’re seeing

How Leading Projects Are Applying ML Today

Leading blockchain projects are already using machine learning to address critical challenges and improve efficiency. As blockchain ecosystems continue to expand, these projects use ML for real-time monitoring, security, and advanced data analysis. 

Here are several examples of how projects are using ML in practice:

1. Codewave

Codewave is a development partner for Multichain, focusing on building scalable blockchain solutions. The company uses machine learning to enhance blockchain infrastructure, optimise data processing, and improve security. 

Codewave’s work includes building decentralised applications (DApps) and integrating blockchain with AI to create more efficient systems. With its expertise, Codewave helps organisations scale securely and build future-ready blockchain ecosystems.

2. Chainalysis

Chainalysis estimates that illicit cryptocurrency addresses received $40.9 billion in 2024. The firm uses ML models to trace flows, assign risk scores, and help law enforcement and compliance teams disrupt financial crime.

3. Nansen 

Nansen reports a database of over 500 million labelled crypto wallets, enabling real‑time alerts on whale activity and token flows.ML engines detect behaviour patterns in these wallets to provide early signals for investment, risk, and due diligence.

4. Forta Network

Forta deploys anomaly‑detection models that analyse thousands of on‑chain events each second to flag risks like flash loans, validator issues, or bridge exploits. These systems turn raw data into actionable security signals.

5. CertiK

CertiK uses ML code‑analysis tools to scan millions of lines of smart contract code, identify patterns of past exploits, and assign risk scores. This accelerates audit processes and improves coverage for developers.

6. TRM Labs 

TRM Labs applies ML across 20+ blockchains, linking transaction flows and identifying suspicious networks. Their system combines graph analysis, behavioural models, and risk thresholds to support institutional compliance workflows.

Why Codewave Stands Out in Blockchain Development

At Codewave, we combine design‑led thinking with blockchain engineering to deliver solutions that integrate seamlessly with your business and evolve as your goals change. 

We don’t just code blockchain, we design the path, align it to your operational needs, and scale it for future requirements.

What Codewave Offers: 

• Official development partner of Multichain, bringing enterprise‑grade blockchain architecture. 
• Deep smart contract expertise with frameworks like Hyperledger Fabric, Ethereum, and Cross‑Chain protocols. 
• Built over 400 applications across industries, serving startups, SMEs, enterprises, and governments.
• Unique focus on business impact: cost reduction, faster development, and increased data transparency. 
• Integration of blockchain with ML & AI for analytical and compliance‑driven capabilities. 

Explore our portfolioto see how we’ve turned complex blockchain challenges into scalable, business‑ready solutions.

Conclusion

Machine learning has the potential to make blockchain data more accessible and actionable, but several key challenges remain. From the difficulty in labelling data to managing privacy concerns, these issues can slow down effective implementation. Yet, with the right approach, these barriers can be overcome. 

The future lies in using machine learning to improve risk prediction, secure transactions, and simplify data analysis across blockchain networks.

Interested in applying machine learning to your blockchain projects?

At Codewave, we specialise in building robust blockchain solutions that integrate seamlessly with your business. Whether you need to secure transactions or optimise data, we’re here to help you design solutions that meet your specific needs. 

Explore how we can work together to turn your blockchain vision into reality.

FAQs

Q: How does machine learning identify fraudulent activity in blockchain transactions?
A: ML detects fraud by analysing patterns in transaction data, such as unusual transaction volumes or addresses linked to illicit activities. It uses anomaly detection techniques to flag suspicious transactions and prevent financial crimes in real time.

Q: What challenges does blockchain data pose for machine learning models?
A: Blockchain data is complex and fragmented, often spread across multiple chains with inconsistent formats. The pseudonymous nature of transactions and the high volume of data make it difficult for traditional ML models to interpret and analyse accurately.

Q: How does machine learning handle blockchain data in real-time applications?
A: ML models process real-time blockchain data using stream processing techniques. By monitoring transaction patterns continuously, they can quickly detect issues such as security breaches, fraud, or network congestion, helping businesses respond faster.

Q: What are the key use cases for ML in blockchain beyond fraud detection?
A: Machine learning in blockchain can be used for optimising transaction efficiency, predicting network congestion, enhancing compliance by tracking illicit activity, and even improving liquidity management in DeFi platforms by forecasting trends.

Q: Can ML models work across different blockchain ecosystems?
A: Yes, ML models can be designed to work across multiple blockchain ecosystems. However, the challenge lies in integrating data from various blockchains, which requires standardising data inputs and using cross-chain technologies for seamless analysis.