In this work, we developed a machine learning interatomic potential (MLIP) for pure silica zeolites, trained on a diverse set of high-temperature ab initio molecular dynamics data encompassing various zeolitic topologies. The MLIP was successfully utilized to simulate the structure, thermal expansion properties, and the effects of pressure on selected zeolites, demonstrating high accuracy and transferability. Notably, the ability of the MLIP to generalize to other zeolitic frameworks was confirmed by its accurate performance on zeolite structures not included in the training set, indicating its potential applicability to a wide range of pure silica zeolite frameworks. The generalizability of our MLIP suggests a promising path for the exploration and development of novel zeolitic materials, with broad implications for the chemical industry and the nanoporous materials modelling community. Indeed, molecular dynamics simulations of zeolites are commonly employed for the characterization of their framework dynamics and response to the application of temperature and pressure, but existing classical interatomic potentials can only describe interactions with limited accuracy, while Density Functional Theory, meanwhile, is accurate but has very high computational cost. The use of MLIP offers an interesting middle-ground choice in the computational toolbox of materials scientists and physical chemists. This will allow, in future work, to revisit the questions of high-throughput screening of zeolites for energetic, mechanical, and thermal stability, as well as the question (still widely open) of experimental feasibility of zeolitic frameworks.