Parkinson’s Disease (PD) is one of the leading causes of death around the world. However, there is no cure for this disease yet; only treatments after early diagnosis may help to relieve the symptoms. This study aims to analyze the impact of feature selection techniques on the performance of diagnosing PD by incorporating different data mining techniques. To accomplish this task, identifying the best feature selection approach was the primary focus. In this paper, the authors had applied five feature selection techniques namely: Gain Ratio, Kruskal-Wallis Test, Random Forest Variable Importance, RELIEF and Symmetrical Uncertainty along with four classification algorithms (K-Nearest Neighbor, Logistic Regression, Random forest, and Support Vector machine) on the PD dataset collected from the UCI Machine Learning repository. The result of this study was obtained by taking the four different subsets (Top 5, 10, 15, and 20 features) from each feature selection approach and applying the classifiers. The obtained result showed that in terms of accuracy, Random Forest Variable Importance, Gain Ratio, and Kruskal-Wallis Test techniques generated the highest 89% score. On the other hand, in terms of sensitivity, Gain Ratio and Kruskal-Walis Test approaches produced the highest 97% score. The findings of this research clearly indicated the impact of feature selection techniques on predicting PD and our applied methods outperformed the state-of-the-art performance.