Modern neurotechnologies generate vast and intricate imaging data across multiple modalities, capturing nuanced aspects of brain structure and function in both healthy and diseased states, thus propelling neuroimaging into the ‘big data’ era. The quantitative analysis of such extensive neuroimaging datasets presents unprecedented opportunities for uncovering novel insights into various neuroscience problems. Machine learning and AI have emerged as promising tools for these analyses, capable of producing personalized, quantitative imaging-based indices with diagnostic and prognostic value, potentially revolutionizing healthcare delivery. However, technical challenges persist due to the high dimensionality of imaging data, disease heterogeneity, and the integration of these technologies into clinical workflows. In this talk, I will discuss novel computational approaches that harness advanced machine learning techniques to tackle these challenges. First, I will present progress toward a scalable unsupervised non-negative matrix factorization framework for interpretable and reproducible dimensionality reduction of neuroimaging data. Second, I will delve into a deep learning framework designed to parse disease heterogeneity and generate subject-specific abnormality maps. Finally, I will explore deep learning models aimed at overcoming obstacles in implementing AI-enabled clinical workflows. I will illustrate the broad impact of these approaches through their applications in diverse settings, with a focus on Alzheimer’s disease.