Remote health monitoring systems are increasingly vital in delivering continuous and personalized care. However, these systems often face challenges in processing uncertain, heterogeneous, and real-time health data. This paper proposes a PSO-Fuzzy Approximate Reasoning Model that integrates Particle Swarm Optimization (PSO) with fuzzy logic to enhance decision support accuracy, convergence speed, and interpretability. The model is evaluated against baseline methods, including PSO-Optimized SVM, Artificial Neural Network (ANN), Random Forest, and Fuzzy Inference System (FIS). Comparative analysis across five performance metrics—accuracy, execution time, convergence, F1-score, and explainability—demonstrates the superiority of the proposed approach. Experimental results highlight its suitability for real-time, interpretable decision-making in remote patient monitoring applications.