Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects motor function, with non-motor symptoms significantly impacting quality of life. Accurate and early diagnosis remains a critical challenge due to the limitations of traditional diagnostic methods. Recently, the integration of deep learning (DL) in healthcare has enabled more precise, data-driven diagnostic systems. This review presents a comprehensive overview of current DL approaches for the early detection of PD, evaluating various data types such as clinical, imaging, audio, and biomarker datasets. We systematically review and categorize 92 studies published between 2019 and 2023, highlight state-of-the-art DL models, discuss their strengths and limitations, and propose a taxonomy to organize the literature. The potential and challenges of DL in PD diagnostics are critically analyzed, offering insights for future research and clinical application.