In this work, we provide strong evidence that the development of machine-learnt potentials can pave the way towards the generation of multiple amorphous MOF models and the study of their properties. Using a dataset consisting of ab initio trajectories of liquid ZIF-4, we trained MLPs that led to an exceptional reproduction of the structural properties of multiple phases -crystal, liquid and several glasses -of ZIF-4. By generating new aZIF-4 models by melt-quenching, we demonstrated the potential of these MLPs to generate disordered models of MOFs at a fraction of the computational cost of AIMD. These models reproduce very well the local structure, framework topology, and macroscopic physical properties (such as mechanical behaviour) of the amorphous MOFs. Modern parallelized MLPs can even allow us to reach model sizes up ten of thousands of atoms at a modest computational cost.