Parkinson’s disease is a progressive neurodegenerative disease resulting in motor symptoms including muscle rigidity, resting tremor, bradykinesia, and postural instability. Given that the diagnosis is primarily clinical, early symptoms can be overlooked, underscoring the need for improved methods of early detection. Wearable biosensor devices that use highly sensitive motion sensors can detect slight motor fluctuations including subtle tremors and gait irregularities. Data collected from these devices can be analyzed by machine learning algorithms to identify motor abnormalities associated with Parkinson’s disease, enabling early diagnosis and improved management. The aim of this study is to develop a wearable biosensor device integrated with machine learning to detect subtle early motor changes that may be linked to Parkinson’s disease. The wearable device is designed to integrate inertial measurement unit sensors (IMU) including accelerometers, which detect tremor amplitude; gyroscopes, which measure angular velocity to determine tremor frequency; and magnetometers, which track posture and rotation. The device also incorporates electromyography (EMG) sensors to monitor rigidity, arm swing, and muscle contraction patterns, in addition to physiological sensors to track autonomic function. Machine learning training will be conducted to allow pattern identification of motor changes consistent with Parkinsonian motor dysfunction. Validity will be assessed by comparing sensor-derived measurements with clinical evaluations to determine its detection reliability. We expect that wearing these devices will enable accurate detection of early Parkinson’s disease symptoms allowing for early treatment intervention and potentially slowing disease progression. Early detection of Parkinson’s disease identifies pre-motor and non-motor signs, occurring before the loss of over 50% of dopaminergic neurons, facilitating timely neuroprotective treatment that may slow symptom progression.