Worldwide osteoporosis stands as a critical skeletal health concern since it displays low bone density while creating enhanced fracture possibilities in affected patients. Patient outcomes require early detection and proper treatment management because it directly lowers morbidity numbers. Dual-energy X-ray Absorptiometry (DXA), provides effective diagnostic results yet this approach remains expensive and unavailable in all clinical locations. Heretofore there exists a vital requirement to develop precise explainable predictive tools which support early disease diagnosis and treatment. The proposed approach unites Generalized Additive Models (GAM) with rule-based models to develop a new method for detecting osteoporosis. GAMs model difficult non-linear patterns linking medical indicators to osteoporosis risk therefore delivering better disease progression understanding. GAM's smooth functions provide clear interpretations about the role of separate risk factors in osteoporosis development. The integration of rule-based models with GAMs produces explicit decision rules that improves both explanatory power and transparency of the predictive model thus making it suitable for clinical implementation. The framework applies to actual clinical data which includes variables from both lifestyle and medical history and demographical information for osteoporosis risk assessment. Accurate model assessment depends on four performance metrics including accuracy, sensitivity, specificity and the Area under the Curve - Receiver operating characteristic curve (AUC-ROC). The framework undergoes comparative assessments with prevailing machine learning models to prove its ability to generate predictions and clinical worth. The research utilizes Data Mining techniques and includes Generalized Additive Models (GAMs) together with Rule-Based Models within a Predictive Framework to evaluate Nonlinear Relationships and maintains Clinical Interpretability standards while achieving results.