In recent years, the increasing amount of cyber threats has highlighted the critical need for effective intrusion detection mechanisms. This paper begins by providing an overview of intrusion detection systems (IDS) and the challenges they face in the modern cyber security landscape. Moreover, it explores the role of ensemble methods, deep learning approaches, and anomaly detection techniques in improving the capabilities of intrusion detection systems. The review concludes with a discussion on emerging trends, open challenges, and future research directions in the field of intrusion detection based on machine learning techniques.