Parkinson’s disease is a complex neurological condition whose initial diagnosis and progression forecasting are very challenging. Accurate detection approaches play a crucial role in early treatment and better patient recovery. Thus, this paper deals with presenting a hybrid machine-learning approach along with multiple algorithms that could elevate the accuracy and credibility of this kind of PD detection. These methods applied include Logistic Regression, Decision Tree, Random Forest, both with Gini and Entropy, Support Vector combined with K-Nearest Neighbours (KNN), Gradient Boosting, CatBoost, XGBoost, and LightGBM. In addition, a Voting Classifier has been used to predict by aggregating those from the above models. The experiments were performed on Parkinson’s dataset, and performance was analyzed with the help of accuracy as well as confusion matrices. The results of the experiments are as follows: complex algorithms like Random Forest, Gradient Boosting, CatBoost, XGBoost, and LightGBM achieved perfect accuracy scores at 100%. The Voting Classifier showed a commendable accuracy of 98.31%, whereas logistic regression performed poorly at an accuracy level of 64.41%. Hence, it suggests that these findings require the use of ensemble learning and the appropriate boosting technique in managing complexities in the data about Parkinson’s disease. These findings reflect the future possibility of hybrid machine learning, which will increase precision for the detection of diseases as well as for predicting progressions. The proposed framework may serve as the basis for building more effective diagnostic tools for better health solutions for Parkinson’s disease.