Efficient inventory management remains a critical and ongoing challenge in the retail sector due to the inherent limitations of traditional inventory systems, including data inaccuracies, human errors, and a lack of real-time visibility. These shortcomings often lead to stockouts, overstocking, and significant revenue losses. To address these issues, this study proposes a smart and innovative inventory management framework that integrates advanced technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and cloud computing. Key IoT components such as smart sensors and RFID tags enable continuous, real-time tracking of inventory, while cloud platforms ensure centralised data access and scalability. AI and machine learning algorithms are applied for demand forecasting, trend analysis, predictive analytics, and intelligent stock optimisation. This technology-driven model not only automates and streamlines inventory processes but also supports dynamic decision-making based on real-time data insights. The framework enhances operational efficiency, reduces inventory-related losses, and fosters a shift towards intelligent, data-driven, and responsive retail management systems.