Digital twins are virtual replicas of actual things, processes, or systems that are generated by integrating real-time data, modeling, and simulation. These digital copies allow for the monitoring, analysis, and optimization of their real-world counterparts. This helps to enhance decision-making and performance in areas like as manufacturing, healthcare, and urban planning. Digital twins have the capability to accurately replicate the actions and condition of their physical counterparts, allowing for the anticipation of maintenance needs, monitoring of performance, and analysis of potential scenarios. They utilize technology such as the Internet of Things (IoT), sensors, data analytics, and machine learning to consistently enhance their virtual models by incorporating real-time data from linked devices. Digital twins are utilized in several domains, such as predictive maintenance of machinery, optimization of manufacturing processes, simulation of medical operations, and even modeling of entire cities for urban planning. Digital twins improve efficiency, productivity, and creativity by offering insights into intricate systems. They also help reduce expenses and dangers involved with physical experimentation or testing. This paper covers a brief implementation of the technologies Digital Twins (DT) along with the Internet of Things (IoT) in solving the food wastage problem that happens during the post-harvest supply chain. About 30% of food gets wasted globally every year from the time it is harvested till it reaches the customer due to poor supply chain management. There is an urgent need to resolve this issue with the help of cutting-edge technologies. This depicts why it is important to reimagine the existing supply chain with detailed implementation and integration of DT. Also, there are relevant case studies and examples to strengthen the idea behind.