Despite remarkable advances in neuroscience and high-throughput omics technologies, the translation of novel biomarkers into routine clinical use remains stubbornly slow and fragmented. While the volume of discovery data continues to grow exponentially, many candidate markers fail to cross the translational valley of death due to a profound clinical utility gap. This narrative review synthesizes recent progress in neuroscience biomarker discovery, evaluating the systemic bottlenecks that hinder clinical translation, ranging from pre-analytical variability and statistical overfitting to complex regulatory and reimbursement hurdles. We propose integrative, reverse-translational frameworks designed to accelerate implementation by beginning with the end clinical context in mind. Emphasis is placed on the shift toward multimodal biomarkers, including the integration of proteomic, metabolomic, advanced neuroimaging, and emerging digital phenotypic data. Through detailed, disease-specific case studies spanning Alzheimer’s disease, multiple sclerosis, traumatic brain injury, and neuropsychiatry, we illustrate both translational successes and persistent methodological pitfalls. We also examine the imperative of global health equity. We argue that biomarker scalability, economic viability, and point-of-care adaptability are just as vital as biological sensitivity in the democratization of precision neurology. Over-reliance on expensive, Western-centric validation pipelines risks exacerbating existing healthcare disparities. Ultimately, we advocate for collaborative, data-driven pipelines that bridge isolated laboratory innovation with pragmatic clinical needs, ensuring that precision neuroscience translates into tangible, accessible outcomes for diverse patient populations worldwide.