Dr.A.Anil Kumar Reddy,K.Vanajakshi,M.Rohitha 02 05(1) This study proposes an innovative and indepth financial statement analysis of Punjab National Bank PNB a prominent public sector bank in India by synergistically integrating traditional financial ratio analysis with advanced Machine Learning ML and Deep Learning DL techniques Recognizing the complex and dynamic nature of the Indian banking sector characterized by evolving credit cycles stringent regulatory frameworks and intense competition conventional financial analysis often provides a retrospective view This research aims to move beyond static analysis by leveraging the power of AI to unearth deeper insights and generate predictive intelligenceThe methodology will involve meticulously collecting and processing PNBs audited financial statements over a significant 510 year period eg FY2015FY2024 A comprehensive set of bankspecific financial ratios will be computed across key dimensions including liquidity eg CreditDeposit Ratio Liquid Assets to Total Assets profitability eg Net Interest Margin Return on Assets CosttoIncome Ratio solvencycapital adequacy eg Capital Adequacy Ratio and asset quality eg Gross NonPerforming Assets Ratio Provision Coverage RatioSubsequently ML algorithms such as Random Forest and XGBoost will be employed These models are adept at identifying intricate nonlinear relationships and will be used to pinpoint the most influential ratios impacting PNBs core profitability and asset quality thereby revealing hidden drivers and potential risk factors Furthermore to provide crucial forwardlooking perspectives Long ShortTerm Memory LSTM networks a specialized form of Deep Learning will be applied LSTMs are particularly wellsuited for timeseries forecasting enabling the models to capture inherent temporal dependencies seasonal patterns eg quarterly fluctuations in deposit growth or credit offtake and market volatilities unique to the banking sector The performance of these AIdriven forecasting models will be rigorously evaluated against traditional linear timeseries models eg ARIMA using standard metrics like Mean Squared Error MSE and prediction accuracy The expected outcome is to demonstrate how AI significantly enhances predictive accuracy and offers dynamic datadriven insights thereby empowering stakeholders with superior tools for strategic decisionmaking proactive risk management and more informed capital allocation within the complex banking landscape Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026