Efficient inventory management is necessary to enhance supply chain performance. Traditional models such as economic order quantity (EOQ), just-in-time (JIT), material requirements planning (MRP), and reorder point (ROP) fail to satisfy the demands in the current dynamic supply chain. This study tries to perform a comparative review of these methodologies in integration with emerging digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), radio frequency identification (RFID), and blockchain. The research evaluates each method in terms of effectiveness, adaptability, scalability and implementation complexity based on various academic and industry sources. Traditional systems remain cost-effective in a stable context; however, they frequently lack the responsiveness required in technology-driven inventory management. On the other hand, digital tools provide greater transparency and predictive capabilities, but they are more challenging due to performance cost and technical barriers. To address this gap, this paper describes both methods and compares them to identify weaknesses and strengths and offers an insight into a hybrid model that integrates the strengths of both paradigms. This approach may facilitate a slight transition toward digitalization, leading to greater resilience and operational efficiency. The findings tend to inform both practitioners and researchers interested in optimizing inventory strategies.