There has been a notable increase in the number of cases of Parkinson’s disease patients in recent years, marking it as a significant neurodegenerative disorder affecting senior citizens globally. Currently, no cure has been discovered for Parkinson’s disease, resulting in the current emphasis on early detection and managing the symptoms. This strategy holds the potential to alleviate its adverse effects and extend the lifespan of affected individuals. Before Artificial Intelligence evolved, Parkinson’s disease diagnosis and prediction required only a comprehensive assessment involving physical examinations, medical histories, neurological tests, and evaluating responses to medication. However, the recent advancements in artificial intelligence allow disease detection, diagnosis, and treatment decisions to be partially or fully delegated to computer agents trained using machine learning techniques. Given these advancements, this study aims to explore the utilization and impact of hybrid deep learning models for predicting, focusing on various features that can be indicative of Parkinson’s disease. Specifically, we examine the prediction of disease-based speech datasets collected from subjects with and without Parkinson’s disease. Using three publicly available datasets from the UCI Machine Learning Repository with nearly identical features, we tested 15 models utilizing five deep-learning algorithms to evaluate the prediction performance. Data imbalances were addressed by employing the SMOTE technique to enforce model regularization. The lime technique for model explainability was applied to highlight the features influencing the model’s outcome. Notably, at least half of the models showed outstanding performance, achieving 100% accuracy, surpassing many existing state-of-the-art techniques in classical and deep learning.