UNLABELLED: Rare neuromuscular diseases pose significant diagnostic challenges due to their genetic complexity and varied clinical presentations. Current diagnostic methods are often costly and inefficient, with primary care physician slacking adequate knowledge and effective diagnostic algorithms needed for accurate identification. This study addresses these challenges by developing PhenoRareAI, a model designed to improve the diagnosis of rare neuromuscular diseases such as Glycogen Storage Disease II and Spinal Muscular Atrophy. A total of 109 electronic health records from Huashan Hospital and external centers were used to develop and validate PhenoRareAI. The model integrates phenotype recognition algorithms and disease priority ranking models, which were rigorously tested against baseline models and validated with external datasets. The key outcome measures included the accuracy of phenotype recognition and disease diagnosis. PhenoRareAI demonstrated signifi cant improvement in phenotype recognition, achieving a micro F1-score and a macro F1-score of 27.40% and 25.16%, compared to the baseline PhenoPro model's scores of 11.44% and 14.65%. In terms of disease prioritization, the model achieved a top 1 ranking for 15 cases of Glycogen Storage Disease II and 17 cases of Spinal Muscular Atrophy after phenotype enhancement, with a mean reciprocal rank (MRR) of 37.37 and 57.21, respectively, significantly outperforming existing methods. PhenoRareAI signifi cantly enhances the diagnosis process for rare neuromuscular diseases, demonstrating strong potential for clinical application. These findings highlight the importance of integrating AI-driven diagnostic tools into healthcare to better navigate the complexities of rare disease diagnosis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13755-026-00455-w.