Parkinson’s disease is a widespread and complex neurological condition characterized by a range of motor symptoms, including trembling, rigidity, and impaired coordination. Additionally, Parkinson Disease affects non-motor functions, such as facial expressions, speech, and mobility, progressively worsening as the disease advances. While commonly associated with older age, Parkinson Disease can manifest in individuals as young as 20, underscoring its diverse demographic impact. This paper aims to provide a comprehensive survey of techniques employed for the detection and analysis of Parkinson Disease. Despite significant advancements in managing Parkinson Disease symptoms through medication and deep brain stimulation, the precise etiology of Parkinson Disease remains elusive, particularly regarding the potential involvement of immunological factors. This ambiguity underscores the intricate nature of neurodegenerative diseases, necessitating continued research to unravel the enigma surrounding Parkinson Disease origins. Notably, this paper highlights the promise of deep learning algorithms, particularly convolutional neural networks and recurrent neural networks, in medical image processing tasks. Deep learning models can discern subtle patterns in medical images, such as brain scans or muscle activity recordings, aiding in Parkinson Disease diagnosis. Finally, this paper provides a comprehensive overview of current methods for diagnosing and analyzing Parkinson’s disease. It emphasizes the challenges posed by the subtle early symptoms and the uncertainty surrounding its etiology. Furthermore, it underscores the potential of deep learning to advance our understanding and treatment of this intricate neurodegenerative condition, paving the way for more precise and tailored medical interventions in the future.