Parkinson’s disease (PD) refers to a neurodegenerative illness that affects both motor and non-motor capabilities, and hence, its early detection boosts the probability of proper management. Our work suggests the usage of a hybrid machine learning model designed to increase the accuracy of diagnosis by evaluating the combined efforts of XGBoost with Multilayer Perceptron (MLP) and Genetic Algorithm (GA). Important speech biomarkers are retrieved and statistically examined for significance like jitter, shimmer, and harmonic-to-noise ratio. The dataset is normalized and then subjected to feature selection, following which dimensionality reduction is undertaken before ultimately carrying out an 80–20 train-test split for the assessment of the best model. The suggested techniques, XGBoost, MLP Classifier, and Genetic Algorithm, have an accuracy value of 96.2%, which is much higher than the accuracy attained by conventional classifiers such as SVM and Random Forest. Sensitivity and specificity were 92% and 94%, respectively. Other parameters, including precision, recall, F1-score, and ROC-AUC, verify the model’s dependability. Such performance gain emerges from the combination of ensemble learning, hyperparameter adjustment, and genetic optimization for classification. This architecture is presented to progress toward web-based and real-world applications and therefore provide an efficient and easy-to-scale clinical diagnostic tool. Future modifications are envisaged to target model generalizability across varied datasets toward an upgrade of diagnostic robustness.