The proliferation of diverse biomedical data modalities-such as genetic, proteomic, neuroimaging, clinical, cognitive, and behavioral data-offers significant potential to advance our understanding of complex diseases and enhance personalized medicine. However, integrating and analyzing such multimodal data presents challenges, often requiring advanced programming skills and tools not accessible to all researchers. We introduce NeuroGeMS (Neuro-genetic Multimodal System), an open-source, user-friendly GUI software designed to simplify multimodal data analysis in biomedical research. NeuroGeMS enables users to analyze complex datasets without specialized programming expertise, integrating state-of-the-art machine learning algorithms and supporting data fusion strategies like early and late fusion. It also offers robust experiment tracking and explainability tools for model interpretation. This paper details the design of NeuroGeMS, highlighting its capabilities in addressing limitations of current multimodal data analysis tools. We demonstrate practical applications through two case studies: predicting early psychosis using cognitive and social data from the LYRIKS dataset, and prognosticating neurodegenerative diseases using cognitive assessments and neuroimaging markers from the ADNI (TADPOLE) dataset. These cases showcase NeuroGeMS’s ability to handle complex datasets, enhance predictive modeling through multimodal integration, and provide interpretable results. By lowering barriers to advanced data analysis, NeuroGeMS aims to democratize access to powerful analytical techniques, fostering broader participation in biomedical research and facilitating the translation of findings into real-world healthcare applications.