PD and ET are neurodegenerative diseases with motor signs, including tremors, making diagnosis difficult. This work uses machine learning to establish an automated method to identify PD and ET by analysing the various traits and patterns that characterise the two conditions. Demographic, medical, and sensor-based motor characteristics are collected to achieve this. These datasets train machine learning models such as SVM, RF, and ANN to learn the complex correlations and patterns of PD and ET. Identifying the most distinguishable traits of PD and ET requires feature engineering. The statistical, time-based, and spectral properties of the motor symptoms of PD and ET are extracted from the data. The features are selected using rigorous methods. Cross-validation measures accuracy, sensitivity, specificity, and AUC-ROC to evaluate machine learning models. Comparative investigations show machine learning algorithms’ strengths and weaknesses in identifying PD from ET. This research is of great therapeutic importance, as an automated and reliable diagnostic tool can help clinicians make accurate and rapid diagnoses. Identifying PD and ET early improves patient care and management. Machine learning models also enable additional data sources and refinement of diagnostic algorithms. Machine learning can identify Parkinson’s disease from Essential Tremor. This research develops an automatic and reliable diagnostic tool using large data sets and powerful machine learning methods. The findings help to distinguish PD from ET, improving clinical outcomes.