The aim is to design a comprehensive shelf space optimization framework that maximizes profitability, enhances sales forecasting, improves efficiency of inventory management, and supports effective decision-making in retail businesses. A robust and interactive analytical dashboard is developed that allows users to visualize critical sales metrics, analyze historical data trends, and accurately forecast product demand and supply requirements based on seasonal variations and sales performance. The work integrates three mathematical optimization paradigms like Linear Programming (LP), metaheuristic search via Genetic Algorithms (GA), and reinforcement learning using Proximal Policy Optimization (PPO) to support both static and adaptive allocation strategies. Experimental validation highlights the relative advantages of each method, with detailed evaluations based on forecast accuracy, inventory turnover efficiency, shelf utilization rate, and overall improvement in profitability. Unlike traditional static optimization models, the PPO-based framework continuously adapts allocation decisions using environmental feedback, improving flexibility in dynamic retail scenarios The paper uses multi-objective shelf optimization considering profitability, utilization, and customer demand simultaneously. The results demonstrate that the integration of predictive analytics and advanced optimization techniques significantly performs traditional shelf management approaches, offering retailers actionable insights and operational advantages.