Parkinson’s disease (PD) is a progressive neurodegenerative disorder that is typified by motor symptoms, cognitive impairment, and autonomic dysfunction, which have a significant effect on the quality of life of the patient. One of the most important aspects of managing PD that is difficult to perform is early detection and constant monitoring as well. Electroencephalography (EEG) and Electrocardiography (ECG) are promising biomarkers that are linked to neural and autonomic perturbations, which can be used in the early detection and monitoring of disease progression. The chapter presents an innovative AI-based solution that involves Change Point Detection (CPD) to recognize important changes to EEG and ECG signals that signal PD progression. In particular, the suggested multimodal model combines the EEG and ECG signals in the form of an autoencoder-based CPD model. The autoencoder is effective in capturing highly dynamic time-dependent information and identifying subtle signal disruptions that are related to PD-related neural and autonomic alterations. The advantage of this methodology is that it combines the EEG and ECG modes, which improves the reliability of biomarkers and the diagnostic accuracy of the method compared to the traditional mode. The overall experimental data show that multimodal CPD is much better than unimodal analysis, which has strong early detection features. The concept of clinical implications also deserves attention in this c4hapter, as it is possible to demonstrate the ways this AI-based framework of multimodal CPD can be applied in practice to diagnose PD in a time-efficient manner, monitor patients individually, and make therapeutic choices more efficiently.