• DEV RUPESH UKIRDE
• KANISHK PRADIP JAWALE

Access to balanced and nutritious diets remains a major challenge for lower- and middle-class populations due to rising food prices, urbanization, and lack of awareness of cost-effective meal planning. This study proposes an AI-driven affordable diet plan framework that integrates nutritional science, generative models, and machine learning to provide cost-conscious yet health sustaining meal recommendations. By leveraging existing research on AI-based dietary recommendations [1] – [12], the study explores the design of a scalable model that ensures affordability, cultural appropriateness, and nutritional adequacy. Experimental results demonstrate that a balanced, budget-friendly diet can be achieved with optimized food selection while maintaining recommended dietary allowances (RDA). The proposed methodology bridges the gap between advanced AI-driven diet personalization and socioeconomic constraints.

Affordable diet, personalized nutrition, artificial intelligence, machine learning, middle class, lower class, dietary recommendation system.

"AFFORDABLE DIET PLAN FOR LOWER CLASS AND MIDDLE CLASS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 10, page no.a184-a186, October-2025, Available :http://www.jetir.org/papers/JETIR2510021.pdf

"AFFORDABLE DIET PLAN FOR LOWER CLASS AND MIDDLE CLASS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 10, page no. ppa184-a186, October-2025, Available at : http://www.jetir.org/papers/JETIR2510021.pdf