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Parkinson’s disease (PD) is a condition that affects the nervous system, making it harder for people to move and control their muscles. Over time, it gets worse and can significantly impact a person's daily life. Detecting and treating the disease early is key to managing it effectively.Most diagnostic processes rely on the clinician’s experience, which is often subjective and inconsistent. In this case, we design a model with optimized feature selection for diagnostic accuracy enhancement in Deep Transfer Learning Based Parkinson’s Disease Detection Model. The system uses deep learning models that have been trained before to automatically recognize and learn the patterns linked to Parkinson’s disease symptoms.An additional feature selection optimization also guarantees that only the relevant attributes are worked with, resulting in decreased computational expenses without loss of accuracy. The method described provides a powerful, effective, and highly accurate non-invasive approach for PD detection which enables early diagnosis and improved patient care.