In the quest for effective and early detection of Parkinson’s disease (PD), machine learning (ML) techniques have garnered considerable attention due to their potential to analyze complex datasets and extract valuable insights. This review paper synthesizes the existing literature on the detection of PD utilizing machine learning methodologies. Previous studies have extensively explored various biomarkers and data modalities, including voice recordings, gait analysis, handwriting patterns, and neuroimaging data, to develop predictive models for PD diagnosis. Furthermore, datasets utilized in previous PD detection studies vary in terms of size, source, and data modalities. Some studies have employed publicly available datasets such as the UCI Parkinson’s telemonitoring data set, which comprises voice recordings and clinical measurements, while others have utilized proprietary datasets collected from medical institutions. These datasets enable researchers to train and validate machine learning models, providing crucial insights into the discriminating power of different biomarkers and feature sets. Despite the promising results, several challenges remain. Data heterogeneity, sample size limitations, and lack of standardization pose significant obstacles. Robust data preprocessing and validation strategies are essential to ensure the reliability and generalizability of the developed models. The review highlights the strengths and weaknesses of various ML techniques, with deep learning methods showing the highest accuracy at 96.60%. The utilization of diverse datasets from sources like Kaggle, UCI, and medical institutions underscores the importance of comprehensive data collection and sharing practices. The findings of this review emphasize the transformative potential of ML in PD detection. Integrating these advanced techniques into clinical practice could enhance diagnostic accuracy, enabling earlier intervention and improved patient outcomes. Addressing the current challenges and leveraging future technological advancements will be crucial in advancing the field of PD detection and management.