Poor indoor air quality (IAQ) and inadequate thermal comfort continue to challenge healthcare facilities in Malaysia, especially in rural areas where poor ventilation and high humidity are prevalent. These environmental stressors negatively impact patient outcomes, staff well-being, and operational efficiency. Existing solutions often overlook the integration of subjective human perception with objective sensor data, resulting in limited adaptability and responsiveness. This study introduces a datadriven, human-centric framework that combines environmental sensor measurements with user-reported comfort feedback to develop predictive models for IAQ and thermal comfort. A comprehensive machine learning pipeline was implemented using four algorithms—Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and Support Vector Machine (SVM)—to model both continuous IAQ indicators and categorical thermal preferences. Experimental results show that Random Forest achieved the best overall performance, with the lowest root mean squared error (RMSE = 14.35) in regression and the highest classification accuracy (87.5%) in predicting thermal preference. Statistical validation confirmed that Random Forest and XGBoost performed similarly in regression, while Random Forest and SVM showed no significant differences in classification accuracy. These findings validate Random Forest as a robust and consistent model across both tasks. This study contributes a validated, AI-enhanced framework for intelligent environmental monitoring in healthcare settings, emphasizing the integration of subjective and objective data streams. The approach supports personalized, data-driven interventions and offers practical insights for facility managers, clinicians, and policymakers aiming to optimize indoor conditions. Future work will focus on scaling the dataset across diverse facilities and climates, enabling real-time deployment, and incorporating explainable AI techniques to enhance model transparency and stakeholder trust.