Laboratory automation is increasingly being utilized by both academia and industry as a driver to increase expediency of chemical reaction discovery and optimization. This movement has been a boon to the area of high-throughput experimentation (HTE) over the last few years, and has fundamentally changed the way that chemists approach optimization and discovery. However, there are drawbacks to the systems currently being utilized. Traditional, batch-based approaches have excelled at screening discrete variables (e.g. solvent, base, catalyst) however they are less effective at screening continuous variables (e.g. temperature, reaction time, photon equivalents concentration) (Figure ). On the flip-side automated continuous-flow reactor platforms, traditionally, are effective at changing continuous variables, but struggle to screen discrete parameters (Figure ). While much work has been done in the development of automated flow reactors for reaction screening and optimization they nevertheless tend to be material inefficient and limited in the screening of discrete chemical parameters, or need to be transferred to another reactor system in order to perform scale up and intensification operations adding redundancies to the process. In response to the challenge of rapidly optimizing reaction conditions, we sought to develop an all-inone multipurpose robotic platform, called RoboChem, that enables the self-optimization, intensification and scale up of photocatalytic transformations (Figure ). This innovative platform overcomes associated challenges by integrating off-the-shelf hardware and customized software, providing a hands-off solution. Our open-source platform combines a liquid handler, syringe pumps, a high-powered photoreactor, inexpensive Internet of Things (IoT) devices, and an in-line nuclear magnetic resonance (NMR) system to enable automated and data-rich optimization. By utilizing a high-power continuous-flow capillary photoreactor, our platform ensures highly reproducible data, effectively mitigating issues related to mass, heat, and photon transport that often contribute to irreproducibility in photocatalytic transformations. To account for complex intercorrelations between reaction variables, optimization algorithms such as design of experiments (DoE) and statistical modeling can be integrated into the platform. However, for complex non-linear relationships, such as those encountered in photocatalytic reactions, machine learning proves to be an even more effective approach. Its ability to rapidly and efficiently analyze vast amounts of data enables the identification of underlying patterns and the extraction of meaningful conclusions. Thus, combining machine learning with reaction automation is advantageous. Given that our platform operates as a linear system (i.e., not parallelized), minimizing the number of experiments required to reach optimal conditions was crucial. For this reason, we turned to Bayesian Optimization, which has gained popularity in the chemistry community due to its ability to optimize black-box functions. As an automated flow chemistry setup, our platform is capable of exploring large regions of the experimental and chemical space within a relatively short period, making it well-suited for addressing complex optimization problems encountered in photocatalysis. The Robochem platform distinguishes itself from current reporting methods by optimizing every substrate, thereby enabling a clear evaluation of the applicability and limitations of the reported transformations, resulting in increased value for industrial implementation. We demonstrate the general applicability of RoboChem to the optimization of a diverse set of photocatalytic transformations, including Hydrogen Atom Transfer (HAT) photocatalysis and photoredox catalysis, which are relevant to medicinal and crop protection chemistry.