Parkinson’s disease is a progressive neurodegenerative disorder that significantly impacts motor and non-motor functions, necessitating early and accurate diagnostic methods to improve patient outcomes. Traditional clinical diagnostic approaches rely heavily on subjective assessment and often detect the disease at advanced stages. In response to these limitations, machine learning and data mining techniques have emerged as promising tools for early detection, particularly through the analysis of biomedical signals such as voice recordings. This study presents a comprehensive exploration of intelligent diagnostic systems that integrate machine learning, feature selection, and metaheuristic optimization techniques to enhance classification performance in Parkinson’s disease detection. Drawing upon existing research, including fuzzy K-nearest neighbor models enhanced by chaotic bacterial foraging optimization and neural network-based voice analysis systems, this work proposes a hybrid analytical framework that emphasizes accuracy, robustness, and computational efficiency. The methodology involves data preprocessing, feature extraction, attribute selection, and classification using advanced machine learning models supported by optimization algorithms. The findings indicate that hybrid approaches combining metaheuristics and machine learning outperform traditional standalone models in terms of diagnostic precision and reliability. Furthermore, the study explores the broader implications of integrating Internet of Things and smart healthcare systems for real-time disease monitoring. Limitations such as data heterogeneity, model interpretability, and scalability are critically discussed. Future research directions highlight the need for explainable artificial intelligence and cross-domain data integration. This research contributes to the growing body of knowledge in biomedical data analytics and provides a scalable framework for early disease detection using intelligent systems.