Parkinson’s disease (PD) is a chronic neurological disease that affects millions of people worldwide characterized by movement impairments, cognitive disability, and decrease the quality of life. PD complexity and the severity of its symptoms demand robust and early diagnostic methodologies and the advancement of new treatment approaches. This survey paper provides a comprehensive and breakthroughs in Parkinson's disease research focusing on innovative diagnostic biomarkers, advanced non-invasive diagnostic techniques and the transformative role of artificial intelligence in enhancing early detection and intervention strategies. The machine learning models such  as CNN, DNN, LSTM, ResNet, PCNN, ALSTM and the optimization methods are GDABC, MASS, QMFO and Swarm are highlighted. The multimodal method used to identify the voice,text,sensor, image or video and Giat data. The paper is also analysed various multimodal techniques useful for classification. The review is identified as a valuable resource for clinicians, researchers and expert to understand the evolving of PD detection and management.