Ensuring compliance with data privacy regulations in assisted living facilities (ALFs) is crucial due to the increasing digitization of patient records and the enforcement of privacy laws such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). Traditional compliance audits rely on manual assessments, which are resource-intensive, subjective, and prone to human errors, necessitating automated risk assessment frameworks. This study presents an AI-driven risk scoring system that predicts privacy compliance risk in ALFs based on facility attributes and regional socioeconomic indicators. A Privacy Risk Indicator (PRI) was formulated to quantify risk levels, and facilities were categorized into Low, Medium, and High-Risk groups using supervised learning models. Comparative evaluation of Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and XGBoost (XGB) models demonstrated that XGB achieved the highest classification accuracy ( \(99.79\%\) ), outperforming other approaches. The analysis revealed that facility capacity, county poverty rate, and unemployment rate are the most influential predictors of compliance risk. The proposed system provides a scalable, interpretable, and automated solution for privacy compliance auditing, reducing dependence on manual inspections. Future research can focus on integrating real-time compliance monitoring, adapting the model to diverse regulatory frameworks, and enhancing model transparency through explainability techniques.