Healthcare organisations must effectively manage their operations to deliver high-quality patient care while keeping expenses in check. Traditional approaches to managing healthcare operations frequently rely on manual procedures and judgement-based decisions, which can result in inefficiencies and less-than-ideal results. Machine learning (ML) approaches have become a potential strategy for improving healthcare operations and resolving these issues in recent years. In-depth research on the use of machine learning to improve healthcare operations management is presented in this paper. The main goal is to investigate how machine learning (ML) approaches might help with resource allocation, scheduling, demand forecasting, and quality enhancement in the healthcare industry. Effective management tools haven't fully materialised yet, despite notable developments in computerization and digitization within the medical industry. Traditional management techniques cannot keep up with the complexity and variability of healthcare operations, thus machine learning methods must be used since they can be customised to fit complicated patterns. Machine learning has two fundamental benefits: building strong models from a large number of weakly predictive characteristics and locating important elements within complex feature sets that directly solve the main operational difficulties. Predicting operational events and finding significant components in workflows are two important operational concerns that are the subject of this study's exploration of this link. By using real-world examples, we demonstrate how machine learning improves human understanding and management of healthcare operations, ultimately resulting in more effective healthcare delivery.