Machine learning (ML) has emerged as a transformative technology, enhancing cybersecurity practices and elevating organizational performance. This study explores ML’s dual role in mitigating cyber threats and driving business outcomes. By reviewing recent literature, the paper identifies how supervised and unsupervised ML techniques enable organizations to predict, detect, and respond to cyber threats with high precision and speed. Additionally, ML contributes to organizational efficiency through data-driven decision-making, predictive analytics, and process optimization, resulting in reduced operational costs and improved customer experiences. However, the paper highlights ethical and regulatory challenges, such as data privacy concerns and algorithmic biases, which create gaps in understanding ML’s comprehensive impact. Addressing these challenges and incorporating innovative technologies like blockchain alongside ML can maximize its potential. The findings underscore ML’s pivotal role in shaping the future of cybersecurity and organizational performance while emphasizing the need for continued research to bridge existing gaps and align technological advancements with ethical considerations.