Parkinson’s disease (PD), a harmful scenario that decreases the value of lifestyle. Those who have this disease are having difficulty in writing, speaking, and walking. According to some research speech analysis is the best technique used to detect the PD because majority of people have speech disorders who are suffering from PD. Detecting the disease in its early stages is the challenge so that it does not get worse. For this, we are using machine learning algorithms for the classification of PD detection. The various classification models like support vector machines, Logistic Regression, KNN, and random forest are effectively used for classification purposes. By using different classification models, we can classify them and predict the accuracy, compare them with other models, and see which best fits for classifying/detecting PD. We are using a dataset in which there are some records based on the voice signals of individuals which helps us to detect who has Parkinson’s or not. Machine learning is very good at recognizing patterns and can identify patterns in data that can help with analysis. We are using different metric calculations such as finding out the precision, f1_score, recall, and confusion matrix as well which gives us an idea about the designed models that we are using, and also accurate results so that can be used for detecting PD.