The influence of technology on our society is significant and ubiquitous in our daily lives. The impact of artificial intelligence (AI) is seen in several domains, such as robots, education, space exploration, business, and healthcare. Due to technological advancements, information may now be rapidly accessible via devices such as wearables, social networking platforms, telephones, and collaborative tools. It has revolutionized networking, travel, healthcare, and communication. The internet of medical things (IoMT) is an emerging field in healthcare that utilizes advancements in the internet of things (IoT). However, the use of technology in medical devices raises concerns around security and data privacy. Healthcare networks are susceptible to several types of attacks, such as brute-force attacks, floods, distributed denial of service (DDoS) attacks, port scanning, and web crawling. These vulnerabilities arise from resource constraints and open connections. This chapter utilizes machine learning (ML) methodologies to enhance the security of healthcare systems and increase the accuracy of intrusion detection systems (IDSs) in addressing these concerns. The study demonstrates that the ensemble learning (EL)-based model known as adaptive boosting (AdaBoost) surpasses other approaches, achieving an exceptional accuracy rate of 99.89% compared to all other models analyzed. Significantly, Bagging and Random Forest (RF) achieve accuracy rates of 99.85% and 99.82%, respectively, demonstrating exceptional precision. The study findings indicate that the suggested IDS works better than other comparable models, as shown by the investigations conducted on the multi-step cyber-attack dataset (MSCAD) dataset.