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Management of bladder cancer changes markedly once a tumor invades the muscularis propria, making reliable separation of non-muscle-invasive bladder cancer (NMIBC) from muscle-invasive bladder cancer (MIBC) a key clinical decision point. Multiparametric MRI (mpMRI), interpreted with the Vesical Imaging-Reporting and Data System (VI-RADS), standardizes local staging by integrating T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI). As evidence has matured, the central challenge has shifted from diagnostic accuracy alone to implementation and clinical utility: ensuring interpretable image quality across scanners and workflows, reducing inter-reader variability, and translating VI-RADS categories into appropriate clinical pathways. Bladder MRI is also being evaluated for treatment response assessment after neoadjuvant systemic therapy and bladder-preserving approaches, supported by emerging post-therapy scoring systems such as nacVI-RADS, quantitative biomarkers, and machine-learning methods. The VI-RADS Quality Score and related quality-control concepts may help separate technically inadequate examinations from biologically equivocal cases. Near-term artificial intelligence applications are likely to be most useful for scan acceleration, automated quality auditing, semi-automated segmentation, and reader support for equivocal lesions. This narrative review synthesizes acquisition and interpretation essentials, diagnostic performance, recurrent pitfalls, and scalable solutions for VI-RADS implementation, response assessment, radiomics, and artificial intelligence.