D. Nandini,M.Rajeshwar Reddy, R.Gowthami 02 06 The stock market is a complex dynamic system influenced by numerous factors including economic indicators sector performance and investor behavior Traditional analysis methods often fall short in capturing intricate patterns and predicting stock price movements accurately This study employs Artificial Intelligence AI Machine Learning ML and Deep Learning DL techniques to perform a comparative analysis of share price trends across various industry sectors By leveraging historical stock data news sentiment and macroeconomic variables the study aims to develop robust predictive models that can identify sectorspecific price movements and market behavior Machine learning algorithms such as Random Forest Support Vector Machines SVM and Gradient Boosting are utilized to analyze large volumes of structured financial data uncovering hidden patterns and relationships among different sectors Deep learning models including Long ShortTerm Memory LSTM networks are applied for timeseries forecasting to capture the sequential dependencies in stock prices The integration of Natural Language Processing NLP techniques further enriches the analysis by incorporating market sentiment from news and social media providing a comprehensive view of factors impacting share pricesThe findings of this study are expected to assist investors portfolio managers and financial analysts in making informed decisions by providing sectorwise comparative insights and accurate share price predictions Additionally the research demonstrates the potential of AIdriven approaches in enhancing traditional financial analysis offering scalable and adaptive tools for the evolving landscape of stock market investment Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026