Improving oil palm productivity without proportionally increasing land and agricultural inputs requires better information for timely and spatially differentiated management. Near-infrared (NIR) spectroscopy has emerged as a promising analytical technology because it can rapidly characterize plant and product properties while reducing reliance on slow, destructive, and chemically intensive laboratory procedures. This qualitative literature review synthesizes peer-reviewed evidence published primarily from 2020 to August 2026 concerning NIR, visible–nearinfrared (Vis–NIR), hyperspectral sensing, and related spectral technologies relevant to oil-palm management. The review integrates direct oil-palm evidence with transferable findings from crop spectroscopy, oil-palm nutrition research, precision agriculture, and technology-adoption literature. The evidence indicates that the strongest plantation-level case for NIR currently lies in rapid foliar nutrient diagnosis, complemented by applications in fruit maturity and quality assessment, disease detection, and multi-scale remote sensing. However, predictive accuracy does not automatically translate into higher fresh-fruit-bunch yield. Productivity effects depend on a longer causal chain connecting reliable spectral measurement with agronomic interpretation, fertilizer prescription, timely field intervention, and subsequent learning. Calibration transfer, environmental variability, genotype and frond effects, external validation, skills, investment costs, input availability, and smallholder accessibility remain important constraints. A closed-loop measure–diagnose–prescribe–act–learn framework and a tiered sensing architecture are proposed. Policy priorities include shared calibration libraries, standardized protocols, independent validation, reference laboratories, shared-service models, workforce development, and multi-estate field trials measuring agronomic and economic outcomes.