aspg-3-4159-1791417438 20261007235718 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 20 2 Elda Maraj Polytechnic University of Tirana, Faculty of Mathematical Engineering and Physical Engineering, Department of Mathematical Engineering, Tirana, Albania Aida Bendo Sports University of Tirana, Faculty of Physical Activity and Recreation, Department of Movement and Health, Tirana, Albania Coronary heart disease, a prevalent cardiovascular condition, affects coronary arteries, causing progression over time. Factors include diabetes, hypertension, inactivity, and tobacco use. Treatment includes medications and surgery, while maintaining a balanced diet and regular physical activity can prevent it. This research aimed to develop and validate a predictive model for CHD occurrence, leveraging the power of multiple regression while considering a range of predisposing variables. This study uses a quantitative, retrospective design utilizing multiple regression analysis to predict the likelihood of coronary heart disease (CHD). The study involved 130 participants aged 24-85, with health history data on cardiovascular risk factors, blood pressure, cholesterol, smoking, BMI, and family history of heart disease. Multiple regression analysis was utilized to determine the significant predictors of CHD diagnosis. Significant relationships between responder variables and predictor factors in a multiple linear function are identified using multiple regression analysis. Our model discovered that a higher risk of coronary heart disease (CHD) was closely associated with both total cholesterol and BMI. The model included factors like systolic blood pressure, diabetes, physical activity, and smoking, but they had lower contributions to the prediction equation, despite cholesterol and BMI being the best predictors. This study successfully developed a multiple regression-based prediction model for CHD that can contribute to a more informed and potentially proactive approach to cardiac healthcare. Further work should focus on refining model accuracy and real-world clinical application. 2025 115 125 4159 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.200210 https://www.americaspg.com/journal/3/article/4159 https://www.americaspg.com/storage/41760878059.pdf