<p>Parkinson’s disease (PD) is a neurodegenerative and non-curable disease seen among the age group of 60 and above with a specific set of observable symptoms. PD is a major health issue worldwide as the average global life expectancy is above 73 years. These challenges call for technology-driven solutions for early detection of PD, which ideally pays off on timely therapeutic procedures and cure. The early diagnosis of PD remains a formidable challenge, given the clinical heterogeneity and subtle prodromal symptoms of this neurodegenerative disorder. Recent advancements in artificial intelligence (AI), especially deep learning (DL) and explainable AI (XAI), offer promising avenues for early PD diagnosis using various data modalities covering hand-drawings, voice biomarkers, gait analysis, and multimodal datasets. This systematic literature review examines 93 high-impact research studies on PD detection, reported in top-tier journals. The analysis of the chosen studies focuses on the major aspects viz. - (1) methodology used for PD diagnosis, including machine learning, deep learning, and hybrid approaches; (2) selection of datasets, including handwriting, voice, medical images, and walking patterns; and (3) validation approaches. Advanced neural networks, particularly DL models have shown high accuracy of 95–99% in handwriting test-based prediction using spiral and wave drawings, and 90–99% in imaging and voice datasets-based prediction. Voice-based systems achieved over 95% accuracy using sound frequency analysis, while combined approaches helped in improving the results on small or varied datasets. By unifying scattered findings, this review offers a clear roadmap towards trustworthy and practical Parkinson’s disease prediction.</p>