S.Chaitanya, Shaik.Aseen Babu, A.Srikanth 02 7(1) Financial modeling forms the backbone of strategic decisionmaking in the investment banking sector Traditionally based on spreadsheets and historical data analysis financial modeling has evolved significantly in the wake of digitization and algorithmic intelligence This study explores the multifaceted applications of financial modeling in investment banking focusing on equity valuation mergers and acquisitions MA leveraged buyouts LBO and risk management As capital markets become more dynamic and complex the reliance on traditional models becomes insufficient to capture realtime fluctuations and highvolume data analytics Hence this research integrates contemporary technological advancements including machine learning ML and deep learning DL to develop predictive financial models Using historical stock data economic indicators financial statements and macroeconomic trends this study employs supervised learning models such as Random Forest Support Vector Machines SVM and LSTM Long ShortTerm Memory networks for sequence prediction The results demonstrate that AIenabled models provide more robust adaptable and forwardlooking financial insights compared to legacy models Ultimately the study proposes a hybrid MLDLbased financial modeling framework that can be embedded into investment banking software to assist analysts and stakeholders in making more informed and datadriven decisions Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026