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Neurodegenerative diseases including Alzheimer's, Parkinson's, and Huntington's disease represent a growing global health crisis, affecting millions worldwide and placing enormous burden on healthcare systems. Early detection remains a critical challenge due to the complexity and symptom overlap among these conditions. This paper presents a comprehensive AI-Based Neurodegenerative Disease Risk Screening System designed to assist in early identification of neurological disease risk through machine learning-based prediction. The system utilizes a synthetic clinical dataset encompassing key parameters including age, memory loss, movement issues, speech problems, family history, and symptom duration. A Random Forest classifier trained on this dataset achieved 93% overall accuracy in predicting the likelihood of three neurological conditions. The system integrates a Time Progression Simulation module projecting disease risk across 6-month, 8- month, and 12-month horizons, and generates a structured clinical-style risk report with explainable AI output. Extensive evaluations confirm high throughput, low response latency (1.2 seconds average), and strong classification performance across all three disease classes.