Introduction: Fibromyalgia syndrome (FMS) represents a complex and challenging condition characterized by widespread pain and an intricate interplay of genetic and environmental factors. Artificial intelligence (AI) offers the possibility of using previous knowledge to understand the molecular underpinnings of pain in FMS. The goal of this study was to leverage this information for drug repurposing prediction, setting the basis for new Clinical Trials. Methods: This study employed an innovative comprehensive approach utilizing AI for a systematic screening of genetic, clinical, and drug-related databases. Genetic marker and recent advancements on mechanoreceptor functioning were used to identify potential targets of pain in FMS that were in-silico assessed for drug repurposing. The approach included a combination of advanced bioinformatics tools that culminated in a bespoke AI screening pipeline that integrates information from diverse sources for a holistic understanding of potential targets of pain in FMS.Results: Our AI screening pipeline unveiled a distinct focus on mechanoreceptors within the dorsal root ganglia (DRG) as potential targets for the therapeutic intervention of FMS. This discovery was further supported by the nuanced analysis of differential gene expression profiles associated with FMS, highlighting the importance of the DRG in mediating pain responses.Conclusions: In conclusion, our study presents a pioneering AI-driven approach for systematically exploring pain-related databases in FMS. The identification of mechanoreceptors in the DRG as potential susceptibility targets opens new avenues for the assessment of drug repurposing data in future Clinical Trials.