Here we describe a new DoE with the combination of a bespoke ML protocol and a reproducible perovskite film fabrication technique that allows for rapid, combinatorial studies and screening of potentially hundreds of thousands of compositions. Our experimental cHTR system can be easily adopted and serve as an inexpensive, cross-reference tool for perovskite material fabrication. The automated approach reduces the sample-to-sample variation of manually produced materials, resulting in better precision of resulting films which could be directly fed into a ML protocol with high degree of trust. Notably, we have shown how the combination of automated perovskite film fabrication and ML modelling can rapidly generate unseen RPPs recipe with improved performance. The ML models elucidate the contributions of key fabrication parameters to make device performance characteristics, aiding the process of device improvement. The complete tracking of the key fabrication steps and conditions and automated performance measurements proposed here generated useful data to train ML models that were subsequently used to design improved PV materials. The insights from the ML models significantly shortened the discovery time and increased productivity. Importantly, our study revealed that some complex compositions, parameters, and additives can be synergistically screened which further accelerates progress in the perovskite photovoltaics. In comparison with manual fabrication, our methodology provides large improvements in the reproducibility and reliability of device manufacture. Moreover, our materials are made in similar conditions to manual drop-casting methods and hence can serve as a state-of-the-art comparison setup. The tracking of all parameters and precise precursor mixing allows for multiple tests and improvements, as well as staggered bleaching. ML protocols significantly narrowed the distribution of parameters that had already been lowered by automation and facilitated fabrication of samples. These results pave the way for rapid, large-scale discovery studies where each fabrication step is carefully recorded and the complete protocol can be reproduced reliably without the variability inherent in manual methods. We have shown that such complex dissection of the fabrication process and automation of crucial steps allows for construction of data libraries that serve as cross-research group reference points and as training data for computational models. In summary, automated fabrication combined ML modeling allows the fabrication of optimal, efficient RPP films of high reproducibility that can be incorporated into devices with very good efficiencies and high long term environmental stability that can readily translate to industrialization of PSCs.