Intrusion Detection Systems (IDS) play a crucial role in safeguarding modern digital infrastructures by identifying potential threats and anomalies in real time. As cyberattacks become more sophisticated, leveraging Machine Learning (ML) techniques in IDS has emerged as a promising approach to enhance detection accuracy, adaptability, and resilience. This paper provides an in-depth exploration of various ML methods applied to IDS, categorizing techniques such as supervised, unsupervised, and reinforcement learning. Additionally, it delves into the most used datasets for training and evaluating IDS models, highlighting their characteristics, advantages, and limitations. Furthermore, the paper addresses the persistent challenges in deploying ML-driven IDS, including issues related to data imbalance, real-time performance, adversarial attacks, and model generalization. Through a comprehensive analysis of current research and future directions, this survey aims to offer insights into the evolving landscape of ML-based IDS, paving the way for more robust and scalable solutions in the face of ever-evolving cyber threats.