Personalized Nutrition Recommendations for Arab Communities: Transforming Diet and Health through Machine Learning

A lack of cultural understanding in traditional nutrition approaches prevents proper adherence to healthy dietary patterns among individuals. The Arab population faces unique challenges due to cultural dietary customs and lifestyle changes that have made obesity and diet illnesses major global public health challenges. The rapid shift to high-calorie but undernourished foods and less physical activity has resulted in alarming rate increases of obesity, cardiovascular diseases, and type 2 diabetes. The lack of cultural understanding in traditional nutrition approaches prevents proper adherence to healthy dietary patterns among individuals. This study focuses to fills the existing gap by applying Artificial Intelligence (AI) and Machine Learning (ML) methods to establish personal dietary recommendations for Arab populations. This research uses advanced AI methodologies to evaluate dietary behaviors while combining cultural food tastes and health status information into personalized dietary recommendations. The dietary recommendation process utilizes four machine learning models comprising SVM and Logistic Regression together with Random Forest and Gradient Boosting. The system creates customized dietary plans by evaluating several aspects including BMI values and health issues and dietary limitations together with caloric consumption information and cultural food choices. Furthermore, the research shows that Artificial Intelligence (AI) implements dietary interventions better than traditional approaches because it produces targeted recommendations which reflect individual culture. The evaluation of the model’s performance measured its accuracy together with precision and recall which yielded encouraging outcomes towards promoting healthier eating habits. To provide fair and ethical recommendations, the implementation requires solutions for data biases, solutions for restricted access to regional dietary data, and transparent algorithms. The study advances AI-driven personalized nutrition research through its framework that integrates AI techniques into culturally appropriate dietary interventions. Future research needs to work on extending data sources while integrating immediate feedback from users alongside advanced AI model development to improve forecasting precision. The implementation of AI-driven dietary systems has the potential to transform public health policies and improve nutritional outcomes for diverse populations.