This comprehensive study explores the utilization of Machine Learning (ML) in the field of cybersecurity, emphasizing its substantial contribution to both defensive and offensive strategies. In contrast to conventional rule-based methodologies, machine learning systems can dynamically adjust to changing threats by acquiring patterns and anomalies from vast datasets. This study investigates the defensive utilization of machine learning (ML) in threat detection, anomaly identification, and security breach prediction. Additionally, it examines the offensive applications of ML, wherein attackers exploit vulnerabilities by applying advanced ML techniques. The study additionally examines the pragmatic implementations of machine learning (ML) in cybersecurity, specifically emphasizing a range of tools such as DeepExploit, Scikit-learn, Metasploit, Nmap, and antivirus software. An assessment is conducted to evaluate the defensive capabilities of Intrusion Detection Systems, firewalls, Security Information and Event Management systems, and email security solutions that utilize Machine Learning. Machine learning in these domains signifies a pivotal advancement in cybersecurity tactics, empowering firms to address cyber risks better.