Urban navigation systems traditionally optimize for efficiency, thereby overlooking environmental factors and personal preferences. This paper introduces Routify, a novel multi-criteria recommender system for personalized urban route selection. Routify advances the state-of-the-art in route recommendation through: (1) integration of diverse environmental data (air quality, noise levels, green spaces) with real-time IoT inputs; (2) a dynamic weighting algorithm allowing fine-grained user control over routing parameters; (3) transparent decision-making via an interactive interface displaying edge-level information; and (4) a community-driven feedback mechanism that continuously refines recommendations. We have implemented Routify as an open-source platform, facilitating further research and development. Experimental results from 1000 origin-destination pairs demonstrate significant improvements in user-defined environmental metrics (up to 32.98% increase in green index for walking and 16.91% reduction in noise levels for cycling) compared to traditional routing systems. These improvements come with slight trade-offs in distance, especially in walking mode. Our research contributes a comprehensive framework for multi-criteria route recommendation, balancing individual preferences, environmental factors, and community insights in urban navigation.