Diabetes mellitus is a chronic metabolic disorder that affects millions of individuals worldwide and poses a significant burden on healthcare systems. Early identification of individuals at risk of developing diabetes is essential for implementing preventive interventions and reducing the likelihood of severe complications such as cardiovascular disease, kidney failure, neuropathy, and retinopathy. Traditional diabetes screening approaches often rely on isolated clinical measurements and may not fully capture the complex interactions among physiological, demographic, and lifestyle-related risk factors. Recent advances in machine learning have enabled the development of intelligent predictive systems capable of identifying high-risk individuals with improved accuracy. However, many existing models provide limited interpretability, making it difficult for healthcare professionals to understand the factors influencing prediction outcomes. To address this challenge, this paper proposes a Machine Learning-Based Predictive Model for Diabetes Risk Assessment Using Clinical and Lifestyle Biomarkers . The proposed framework integrates clinical biomarkers, including glucose level, insulin concentration, blood pressure, body mass index, and HbA1c, with lifestyle-related factors such as physical activity, smoking status, dietary habits, and family history. Following data preprocessing and feature selection, a machine learning classification model is developed to predict diabetes risk. An explainability module based on SHAP and LIME techniques is incorporated to provide transparent and interpretable predictions. The framework is evaluated using publicly available diabetes datasets and standard performance metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Experimental results demonstrate that the proposed AFJBS framework achieves superior predictive performance while maintaining interpretability, thereby supporting reliable and informed healthcare decision-making