Machine Learning in Stock Market Forecasting: A Comprehensive Review

Forecasting the stock market has always been a complex challenge due to its volatile, nonlinear, and ever-changing nature. In recent years, machine learning and deep learning have transformed how researchers and practitioners approach this problem. This review brings together the latest developments in these fields, showing how models such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformers outperform traditional methods like ARIMA and GARCH by learning intricate market patterns. It summarizes key studies across major financial domains—including stock indices, individual equities, commodities, and cryptocurrencies—while examining the growing role of hybrid and ensemble models that combine technical indicators, macroeconomic data, and even sentiment from news and social media. Beyond model performance, the review highlights ongoing challenges such as model interpretability, uncertainty measurement, and real-time scalability. It also explores emerging frontiers like quantum and reinforcement learning, AutoML, and explainable AI. Overall, this article offers a clear and up-to-date overview of how artificial intelligence is reshaping financial forecasting and provides guidance for future research in building adaptive, transparent, and reliable prediction systems. JEL Classification: C45, C52, C53, C58, G17