Type 2 Diabetes is a global health concern due to its increasing prevalence among the global population. The number of diabetic patients has continuously increased over the past 21 years. The maintenance and treatment of diabetic patients take into account factors such as the duration of diabetes, food preferences, cultural considerations, and dietary restrictions. Inadequate management of the dietary intake among individuals with diabetes may escalate to a more severe medical state. This work proposes a personalized food recommendation system framework (FoodRec-f-T2D). It suggests food menu based on both user and health contexts to prevent, maintain, and balance blood sugar levels for type 2 diabetic patients. The framework utilizes a Context-Aware Recommendation System with the post-filtering concept. We evaluated the framework on 25 possible use cases based on health contexts using combinatorial interaction method. The results show the Acceptability Rate of 100% and the Accuracy ( \(\text {sim}_{\text {macro}}\) ) of 0.83, on average.