The fast expansion of networked systems and the growing complexity of cyberattacks have made cybersecurity a major problem in many different industries in the digital age. Conventional approaches to threat detection and response, which often depend on preset rules and signature-based detection, are showing themselves to be inadequate in the face of current cyber adversaries' developing tactics, techniques, and procedures (TTPs). The number and complexity of today's cyber threats are too much for these traditional ways to handle, which is driving up demand for more sophisticated and flexible security solutions. This study explores how machine learning (ML) may improve cybersecurity measures by providing a thorough examination of how ML can revolutionise threat detection, prediction, and mitigation. Cybersecurity systems can now analyse enormous volumes of data in real-time, spot patterns suggestive of malicious behaviour, and respond to new and emerging threats with previously unheard-of speed and precision by using the power of ML algorithms. A thorough examination of machine learning (ML) approaches is conducted, highlighting the distinct contributions that supervised, unsupervised, and reinforcement learning make to different facets of cybersecurity.