This thesis highlights the advantages and disadvantages of machine learning (ML), which is crucial to cybersecurity. In addition to outlining the ways in which ML's flexibility and pattern recognition might bolster defences, it also highlights potential hazards such as biases, data poisoning, and privacy concerns. The study examines a range of attack and defence scenarios, including both proactive and reactive approaches to ML system security. Responsible AI is promoted by discussing ethical issues, such as the necessity of transparency. As a potent instrument in contemporary cybersecurity, the thesis promotes constant improvement to guarantee that ML is robust against new cyberthreats.