The expansion of financial markets and the simultaneous increase in complex stock market offenses necessitated the development of innovative detection and prevention measures. This review article offers a comprehensive analysis of advancements in the intersection of stock crime detection and machine learningMachine learning methodologies for the last thirty years. This research attempts to thoroughly investigate the progression of machine learning applications in the identification of fraudulent activities, insider trading, and market manipulation. This examination is conducted through an in-depth investigation of relevant literature, empirical case studies, and rigorous methodological assessments. The historical trajectory demonstrates the evolution of countermeasures devised in response to affluent individuals engaged in illicit financial activities. The field of machine learningMachine learning has demonstrated a continuous progression to effectively respond to the intricate dynamics observed within financial markets. The advancement of statistical methodologies and ensemble techniques in subsequent years played a vital part in overcoming the gap between the initial rule-based systems and the transformative capabilities of deep learning models. The paper highlights the significant role that machine learningMachine learning may play in enhancing the capabilities of market participants and financial regulatory bodies, which enhances the precision of predicting market irregularities and detecting fraudulent conduct by facilitating the analysis of extensive volumes of financial data. This paper addresses ethical concerns about data protection, neutrality, and transparencyTransparency in utilizing these tools. The provided text serves as an initial foundation for comprehending the historical context, present challenges, and prospective advancements in the realm of preventing stock market crime.