Parkinson’s disease (PD) is a chronic neurological disorder and one of the most significant health problems in the world. It also leads to brain disorders, and some body parts will be affected by non-functioning. Recently, machine learning methods have played a significant role in biomedical engineering and the diagnosis of various medical disorders in the present generation. The Artificial Neural Network (ANN) and machine learning methods are used in this paper to determine PD disease. Classification methods such as ANN, Light Gradient boosting machine (LGBM), Extreme Gradient boosting (XG Boost), and Random Forest are used to predict the accuracy of the disease. This chapter’s main idea is to use ANN to reduce the error possibility in the training dataset, the accurate prediction of machine learning methods is analysed in this paper, and the various machine learning methods are used to predict PD and its performance metrics, such as precision, Recall, F1 score, Area under region of curve (AUC), and Accuracy is calculated. This article aims to decide which machine learning algorithm can give an accurate prediction for people with PD. This paper uses ANN, XGboost, and LGBM models to predict PD. The ANN generates 97.2% accuracy, XGBoost accuracy is 96.6%, LGBM has accuracy of 94.9%, Gaussian Classifier accuracy is 88.9%, Decision tree accuracy is 86.44%, Voting Classifier and Random Forest have accuracy of 93.22% and 96.43%, the logistic regression and support vector machine have less accuracy of 84.61% and 87.17% only. The proposed ANN is compared with conventional methods with its accuracy, and it is concluded that Artificial Neural Network models can be used to predict Parkinson’s disease early.