S.Raj Kumar, M.Rajeshwar Reddy,R.Gowthami 02 7(1) The financial statement analysis of a company serves as a vital instrument in assessing its performance sustainability and longterm value creation for stakeholders In this research we conduct a detailed financial statement analysis of Hero MotoCorp Ltd the worlds largest manufacturer of twowheelers by integrating conventional ratio analysis with advanced softwareenabled techniques including machine learning ML and deep learning DL The purpose of this hybrid framework is to not only evaluate historical and current financial performance but to also develop models that can forecast future trends detect anomalies and support strategic decisionmaking Traditional financial analysis is often constrained by human interpretation limited dimensions and backwardlooking metrics To counter this we utilize intelligent systems that leverage historical data industry variables macroeconomic factors and financial indicators to build predictive models ML algorithms such as Random Forest and SVM assist in classification and regression tasks while deep learning models like LSTM offer robust forecasting capabilities The integration of NLPbased sentiment analysis further enriches insights drawn from financial news and earnings calls This approach is novel in the Indian automotive context and provides Hero MotoCorp with a framework for automated financial monitoring credit risk profiling and strategic planning Our results highlight how data science and financial expertise can converge to create intelligent platforms that reduce decision latency and increase financial accuracy thereby setting a new benchmark in corporate finance Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026