Fitness enthusiasts often face difficulty constructing daily meal plans that both meet specific goals (bulking, cutting, maintenance) and respect personal constraints such as allergies. This study presents Nutri-Fit, an ontology-based expert system that models nutritional knowledge and applies SWRL rules and Pellet reasoning to generate personalized menu recommendations. Knowledge was acquired from a domain expert and literature, formalized in OWL with classes for Users, FoodItems, Targets, and Allergies, and encoded rules for allergy exclusion and macronutrient-based recommendations. The system was functionally evaluated using the Pellet reasoner and tested on six representative user profiles; the evaluation focused on the system’s ability to exclude allergenic foods from recommendations. Results show the system correctly excluded allergenic items for all tested profiles (Allergy Exclusion Success Rate = 100%) and produced target-aware recommendations for bulking, cutting, and maintenance. This work demonstrates the feasibility of an ontology + SWRL approach as a knowledge-rich foundation for personalized dietary recommendation for fitness enthusiasts, and it outlines limitations and directions for quantitative, user-scale evaluation.