The development and commissioning of control systems for long overland conveyor belts present significant engineering challenges, particularly in mining operations where conveyors must transport material over extended distances and varying elevations. Traditional approaches to control system design often struggle to address the complex dynamics introduced by variable loading, gradient changes, and the need for precise belt tension management. This paper presents a comprehensive methodology that leverages Model-Based Design to support the development, evaluation, and deployment of advanced process controllers (APCs) for overland conveyor systems. A detailed dynamic model of the conveyor is developed, incorporating critical subsystems such as belt tension regulation, pulley slip dynamics and variable material loading. The model includes typical head-drive and tripper configurations, capturing the interaction between multiple electric drives and the conveyor belt. By simulating these elements, the model provides a high-fidelity platform for understanding system behaviour under a wide range of operating conditions, including disturbances caused by fluctuating loads, gradient changes, and mechanical wear. Model-Based Design enables iterative development and rigorous testing of control algorithms in a virtual environment prior to hardware implementation. Control strategies can be evaluated for robustness and reliability using the validated conveyor model, significantly reducing the risk of software-induced fault and downtime during commissioning. Feedforward and model predictive control concepts can be applied efficiently by using reduced-order models tailored to the conveyor dynamics. The methodology supports automatic code generation, allowing the tested control logic to be deployed to production-grade programmable logic controllers (PLCs). This workflow ensures that controller software is robust and maintainable, with traceability from system requirements through to deployed code. Validation of the conveyor model is performed using field data, ensuring that simulated responses accurately reflect real-world system dynamics. This validation step is critical for building confidence in the model’s predictive capability and for supporting the development of advanced control strategies tailored to site-specific requirements. The proposed workflow also establishes a regression testing environment, enabling continuous verification of controller performance in desktop simulations and on the deployed PLC hardware. This systematic approach reduces development time and cost by identifying and resolving issues early in the design process, while improving software quality and reliability through comprehensive simulation and testing. In summary, the paper demonstrates the substantial benefits of applying Model-Based Design to the development of control systems for long overland conveyor belts. The approach facilitates integration of multidisciplinary engineering teams, accelerates the adoption of advanced control technologies, and ultimately delivers more reliable and efficient conveyor operations. The methodology and results presented are broadly applicable to similar industrial automation challenges, offering a scalable framework for future APC development projects.