To address these challenges, our research focuses on developing methodologies to accelerate the discovery of polar organic cocrystals, utilizing advanced computational tools and high-throughput (HT) screening approaches. Recent advancements have driven the integration of machine learning (ML), HT computations, automated experimentation, and robotic synthesis to accelerate materials discovery. These approaches enable predictive modelling, rapid screening, and autonomous fabrication, significantly improving efficiency and precision while minimizing reliance on traditional trial-and-error methods, thereby expediting the discovery of new functional materials. We 5 propose a data-driven approach to systematically explore and optimize the design space of polar cocrystals, enabling a more efficient discovery and refinement of novel compounds. In this approach, we analyzed datasets to identify critical features influencing polar cocrystal formation, such as the HOMO-LUMO energy gap, by using ML algorithms , and thus guiding the selection and design of new cocrystals with high potential for success. Applying ML models to extensive datasets enables 10 us to predict the likelihood of multicomponent crystal formation, facilitating the filtering and prioritisation of target molecules with high co-crystallization potential. We further accelerate the crystallization process by testing numerous variables simultaneously on a high-throughput (HT) automation platform, generating extensive data to enhance predictive models. The intergration of HT automation with advanced imaging and diffraction methods ensures rapid detection of 15 crystallization events and verification of cocrystal formation, reducing reliance on conventional iterative methods and providing a systematic and scalable pathway for discovery.