Parkinson’s Disease (PD), a progressive neurodegenerative condition, sometimes eludes early diagnosis owing to its subtle and diverse motor symptoms. Conventional diagnostic methods are expensive, intrusive, and often unattainable. This paper presents the NeuroKey Prediction Tool, a lightweight, browser-based AI system that utilises passive keystroke dynamics gathered during regular typing to detect and track Parkinson's disease. Utilising a modular Streamlit framework, NeuroKey extracts statistical and temporal biomarkers, including key hold time and inter-key intervals, employing machine learning and deep learning models for classification and regression applications. The Random Forest classifier attained an accuracy of 88% in Parkinson's Disease detection, surpassing XGBoost (65%) and Logistic Regression (47%). In terms of predictive performance for motor severity estimation (UPDRS/nQi scores), Ridge Regression (R2 = 0.75), LSTM (0.74), and Random Forest Regressor (0.69) exhibited robust results. These findings validate the feasibility of digital phenotyping using typing behavior as an effective, non-invasive biomarker for neuromotor evaluation. NeuroKey’s lightweight, scalable, and privacy-conscious architecture facilitates home-based ambient assisted living and telemedicine processes, thereby improving proactive, patient-centered treatment. Future endeavors will enhance functionality by utilizing varied, real-world typing data, incorporating personalized baselines, and investigating federated learning to protect privacy while augmenting performance. NeuroKey showcases the integration of AI and IoT for accessible, continuous, and precise neurological healthcare.