A Hybrid Security Model: Leveraging Blockchain and Machine Learning Models for Safe Transactions

Digital transactions happen more frequently so security measures and fraud prevention along with data protection need to be at the highest level possible. Modern security controls struggle to address emerging cyber threats because of their inflexible operation. This document outlines an integrated blockchain and machine learning model design for developing flexible and extensive protection measures for secure online transactions. Through blockchain mechanism organizations achieve decentralization alongside immutability and transparency which helps prevent risks connected to unauthorized access and data manipulation. The security benefits of ML models include instant fraud detection while they identify anomalies and predict threats so systems can take active measures against cyber threats. This research examines different ML methods beginning with supervised learning and proceeding to anomaly detection and deep learning so as to boost transaction surveillance and automation security. Besides the paper offers suggestions for smooth implementation of strategies to address scalability issues and computational performance problems alongside integration challenges. The hybrid method delivers improved transaction security through enhanced protection and simultaneously produces reduced false positive incidents and speeds up cyber threat response times. This research unites blockchain's trustless system with ML's intellectual ability to develop a modern security solution for financial deals and digital asset protection and e-commerce transactions.