Parkinson’s disease (PD) is a degenerative neurological condition that impairs motor functions and reduces quality of life. The accurate and prompt identification of Parkinson’s disease is critical for effective therapy and better patient outcomes. This study investigates gait pattern analysis as a non-invasive way of identifying Parkinson’s disease. Complete gait data is taken from the PhysioBank repository, where data is collected using wearable sensor technology, including pressure-sensitive insoles, which capture ground reaction forces that reflect foot movements during gait. Gait data from Parkinson’s disease patients and control participants were rigorously preprocessed to assure data reliability. The performance of various machine learning models for categorizing gait patterns, including Random Forest, XGBoost, CNN, LSTM networks, and Decision Trees is assessed. The work underlines the relevance of interpretability in machine learning models in therapeutic settings, using LIME and SHAP approaches to explain model decisions. This study not only advances biomedical informatics by combining cutting-edge machine learning models with interpretability approaches, but it also lays the framework for future advancements in customized healthcare and assistive technologies. The proposed ensemble model, which combines the characteristics of multiple individual models, produced an outstanding 96.34% accuracy, exceeding existing approaches and indicating substantial potential for real-time Parkinson’s disease monitoring and diagnosis.