B. Ganesh1, V. Karthik2, M.S. Sushma Sailu3, S. Chanukya4, M. Kalyan Kishore5 03 3(1) Advanced cybersecurity issues that modern banking applications face include SQL injection crosssite scripting adversarial machine learning attacks and attempts to compromise credentials Conventional logging methods undermine incident investigation and regulatory compliance due to mutability issues and a lack of forensic integrity This study offers a novel architecture that creates immutable evidence collection for cyber defense by fusing blockchain technology with machine learningbased threat detection The suggested system automatically logs attack evidence on a private blockchain ledger and uses Random Forest classifiers to detect malicious activity in realtime The implementation makes use of MongoDB for operational data storage Python based ML microservices Nodejs backend orchestration and Reactjs frontend Using cryptographic verification smart contracts guarantee the persistence of unchangeable evidence Experimental validation demonstrates 96 detection accuracy across various attack vectors with subsecond response latency The blockchainanchored evidence provides nonrepudiable forensic trails suitable for audit and legal proceedings addressing critical gaps in conventional security information management systems Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026