As supply chains strive for continuous improvements to maintain their strength and their ability to compete in the market. Optimization of different operational concepts and practices has become strategically vital. This importance increases dramatically in small to medium industries (SMEs) sector, which severely struggles to compete in the market due to managerial challenges. Accordingly, this research aims at developing a framework for inventory optimization in the SMEs manufacturing sector based on game theory, gamification, and multi-agent reinforcement learning simulation models. The game theory will be used to set strategies among different stakeholders within the supply chain; gamification is used to increase user motivation by applying game elements to a digital data collection system, while reinforcement learning techniques will be employed to set policies for inventory estimation in terms of reorder point, inventory ordering cost, and inventory level, upon which inventory optimization can be achieved. This paper conducts a systematic review of inventory optimization, highlights some limitations of current approaches and then concludes with the proposed framework to overcome these limitations. The systematic review revealed that there is no comprehensive framework for inventory optimization in the SMEs manufacturing sector, using “Reinforcement Learning and Game theory techniques". Practitioners can benefit from this model to optimize their inventory and make corrective actions considering inventory management system, particularly in SMEs.