A.Srikanth, Dr.M.Pavani ,G.Uma Maheshwari 02 05(1) Financial analysis is fundamental to understanding the performance and stability of banks Traditionally it involves examining financial statements ratios and market trends to assess profitability liquidity and risk However the banking sector today operates in a highly datadriven environment where conventional methods may not fully capture complex hidden patternsThis study begins with a comprehensive financial analysis of Axis Bank using traditional tools and techniques followed by an extension into Machine Learning ML and Deep Learning DL methodologies By leveraging historical financial data stock prices and macroeconomic indicators the study builds predictive models using algorithms such as Random Forest Support Vector Machines and Long ShortTerm Memory LSTM networks These models aim to forecast future trends detect anomalies and identify significant factors influencing Axis Banks performanceThe integration of ML and DL enhances the depth and accuracy of the analysis demonstrating the transformative potential of artificial intelligence in modern financial research and decisionmaking Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026