Background/Objectives: Gut dysbiosis has been implicated in multiple sclerosis (MS), but microbial signatures remain inconsistent across studies. Machine learning (ML) algorithms based on global microbiome data integration can reveal key disease-associated microbial biomarkers and new insights into MS pathogenesis. This study aimed to investigate gut microbial signatures associated with MS and to evaluate the potential of ML for diagnostic applications. Methods: Fecal samples from 29 relapsing–remitting MS patients during exacerbation and 27 healthy controls were analyzed using 16S rRNA gene sequencing. Differential abundance analysis was performed, and data were integrated with 29 published studies. Four ML models were developed to distinguish MS-associated microbiome profiles. Results: MS patients exhibited reduced levels of Eubacteriales (p = 0.037), Lachnospirales (p = 0.021), Oscillospiraceae (p = 0.013), Lachnospiraceae (p = 0.012), Parasutterella (p = 0.018), Faecalibacterium (p = 0.004), and higher abundance of Lachnospiraceae UCG-008 (p = 0.045) compared to healthy controls. The Light Gradient Boosting Machine classifier demonstrated the highest performance (accuracy: 0.88, AUC-ROC: 0.95) in distinguishing MS microbiome profiles from healthy controls. Conclusions: This study highlights specific microbiome dysbiosis in MS patients and supports the potential of ML for diagnostic applications. Further research is needed to elucidate the mechanistic role of these microbial alterations in MS progression and their therapeutic utility.