Parkinson’s Disease (PD) is a neurodegenerative disorder that displays intricate motor symptoms and non-motor symptoms, making diagnosis difficult both in terms of timeliness and precision. Modern advancements in biomedical engineering, together with computational analysis, support the use of various diagnostic signals for early detection of PD. This study examines five commonly evaluated PD indicators, which include voice patterns, alongside Magnetic Resonance Imaging (MRI) results, Electroencephalogram (EEG) output, and spiral drawing tests, together with walking assessments. The evaluation analyzes each diagnostic method through its relation to physiological factors and provides details on acquisition methods and features that lead to Machine Learning and Artificial intelligence-based diagnostic results. This review describes essential progress together with advantages and disadvantages of each method, as it shows how these methods function in real world clinical practices. This paper identifies major obstacles within these systems, including variations in data quality and difficulties with standardization, together with interpretation, limitations of models that hinder their widespread implementation. This research establishes a practical guide for scientists and medical professionals who pursue the development of non-invasive, cost-efficient, and modality-oriented detection approaches for Parkinson’s disease.