The interaction of genetic predisposition and modifiable lifestyle factors like diet, physical activity, and obesity drives the complex and rapidly expanding global health challenge known as type 2 diabetes (T2D). Traditional predictive models often fail to account for the synergistic relationship between these factors, limiting their utility in early detection and personalized prevention strategies. This study presents a novel machine learning (ML)-based framework that integrates lifestyle and genetic data to enhance the precision of T2D onset prediction. A large dataset with lifestyle behaviors, genotypic single nucleotide polymorphisms (SNPs), and clinical parameters were used to create and test several “machine learning algorithms, such as Random Forest (RF) and Gradient Boosting (XGBoost).” Advanced feature selection methods identified critical predictors, including BMI, specific SNP markers, and physical activity levels. It did better than single-domain models, with an area under the curve (AUC) of 95%, a precision of 91%, and an F1-score of 92% in both internal and external validation cohorts. Additionally, feature importance analysis provided actionable insights into the interplay of risk factors, facilitating interpretability. By harmonizing lifestyle and genetic data, the proposed framework significantly advances the accuracy and applicability of predictive modeling for T2D. This approach bridges the gap in heterogeneous data integration, paving the way for personalized prevention strategies and targeted interventions. Future work will explore the model’s scalability to diverse populations and the inclusion of real-time data from wearable devices, further supporting its clinical relevance and adoption in precision medicine.