The paper is presented with a comprehensive Markov Chain framework for optimizing inventory management systems in the furniture industry. A stochastic model has been developed that captures the dynamics of daily demand fluctuations, restocking policies, and cost structures related to high-value items like dining tables and chairs. The daily demand (λ=12 units per day) has followed a Poisson distribution. A model of a three-state inventory system with an (s, S) policy where “s” = 5 (reorder point) and “S” = 30 (order-up-to level) has been generated, incorporating holding costs of 25 BDT per chair per day and stock-out costs of 8,000 BDT per unit sale loss, reflecting the typical high storage requirements and profit margins of furniture retail sales. The analysis has shown the stationary distribution of inventory states and calculated expected daily costs with optimal reordering strategies. The final results have also shown that retailers can reduce overall costs while keeping service levels above 95% by using Markov Chain modeling to balance holding costs against stock-out risks.