Zero-knowledge machine learning (zkML) in blockchain has several practical use cases. It enables private data processing, allowing AI models to analyze sensitive data without exposing it, ideal for healthcare or finance. zkML ensures verifiable computations, proving model outputs are accurate without revealing inputs, enhancing trust in decentralized systems. It supports secure on-chain predictions, like fraud detection, while preserving user privacy. zkML also facilitates privacy-preserving smart contracts, enabling complex logic without compromising data confidentiality. Additionally, it can optimize decentralized marketplaces by matching buyers and sellers privately. By combining zero-knowledge proofs with machine learning, zkML ensures scalability, security, and privacy, making it valuable for DeFi, supply chain transparency, and secure voting systems on blockchain.