Mathematical modelling plays a critical role in the design, optimisation, and control of dynamic systems in the process industry. While mechanistic models offer strong explanatory and predictive power, their effectiveness depends on informed model selection and precise parameter calibration. Model-based design of experiments (MBDoE) provides a framework for addressing these challenges by designing experiments that accelerate model discrimination and parameter precision tasks. However, its practical application is frequently constrained by fragmented digital tools that lack integration and make MBDoE implementation a task for expert users. To address that – thus supporting the widespread use of MBDoE – MIDDoE, a modular and user-friendly Python-based framework centred on MBDoE is introduced. MIDDoE supports both model discrimination and parameter precision design strategies, incorporating physical constraints and non-convex design spaces. To provide a comprehensive MBDoE digital tool, the framework integrates numerical techniques such as Global Sensitivity Analysis, Estimability Analysis, parameter estimation, uncertainty analysis, and model validation. Its architecture decouples simulation from analysis, enabling compatibility with both built-in and external simulators, which allows MIDDoE to be applied across different systems. MIDDoE practical application is demonstrated through two case studies in bioprocess and pharmaceutical systems for model discrimination and parameter precision tasks.