Digital Twin (DT) technology combined with the Internet of Things (IoT) can be used to provide new solutions to real-time monitoring and management of agriculture. This paper introduces an IoT-based digital twin platform that will help in streamlining agricultural operations by incorporating different sensor technologies to monitor vital soil and crop conditions, such as moisture, temperature, pH, and nitrogen concentrations. The system offers predictive analytics to inform irrigation control, pest control, and fertilizer application, to help in making agricultural activities more sustainable. The effectiveness of the model is tested based on real time data integration and predictive modeling with 92% accuracy in monitoring soil moisture and an 87 percent accuracy in predicting crop yields. Although the system shows a high potential in terms of resource optimization and productivity, issues like sensor calibration, network connectivity and scalability to bigger operations exist. The future direction must be aimed at making sensors more reliable, more scalable, and adding AI and automation to make the system even more efficient and applicable in precision farming.