This research proposed a complete healthcare data analytics context leveraging machine learning (ML) techniques to improve patient results and enable real-time monitoring. Smart healthcare leverages IoT, AI-driven predictive analytics, real-time anomaly detection, personalized recommendations, remote monitoring, optimized resource allocation, and secure data management to enhance patient outcomes and enhance healthcare efficiency. Moreover, the proposed method integrates feature selection approaches, predictive modeling, and optimization techniques to advance decision-making in vital healthcare scenarios. Moreover, Principal Component Analysis (PCA) and Genetic Algorithm (GA) were implemented for feature selection, while Random Forest and Gradient Boosting were applied for predictive modeling. The results establish high predictive accuracy, with performance metrics ranging from 85% to 98%, showcasing the efficiency. Furthermore, Difference detection techniques like Isolation Forest were utilized for real-time monitoring, providing actionable insights.The researchsubsidizes optimizing healthcare resource allocation through Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) along with comparative analysis favouring PSO for faster convergence. At last, the findings support the combination of machine (ML) learning contexts in healthcare for improved patient administration and operational effectiveness.