Parkinson’s disease is a chronic, progressive neurological disorder affecting principally the motor system. It is distinguished by symptoms such as muscular rigidity and slowness of movement. As per statistics provided by the World Health Organization, Parkinson’s disease affects around 6 million individuals globally. The incidence of this pathology increases with age, and it mainly affects people over 60. The consequences of Parkinson’s disease on patients’ quality of life are significant, causing functional limitations, communication problems, and psychological impacts. As a result, early diagnosis of this condition is critical to ensure appropriate treatment and boost the overall life satisfaction of patients. In this paper, we suggest a method based on machine learning techniques for recognizing Parkinson’s disease within a clinical dataset. To overcome the imbalanced classes in the dataset, we applied the SMOTE method to generate synthetic examples of the minority class. Next, we employed Featurewiz and the genetic algorithm to identify the most relevant attributes. Finally, we optimized the hyperparameters of the models with Grid Search. The experimental findings demonstrate that our strategy outperforms traditional approaches and significantly boosts the efficiency of the various performance metrics that exceed 96%. This promising approach provides new prospects for early and accurate diagnosis of Parkinson’s disease, enabling earlier therapeutic intervention and enhancing patients’ life satisfaction.