A supervised learning approach for dynamic sampling (SLADS) yielded a seven-fold reduction in the number of pixels sampled in hyperspectral Raman microscopy of pharmaceutical materials with negligible loss in image quality (~0.1% error). Following validation with ground-truth samples, sparse sampling strategies were informed in real-time by the preceding set of measurements. In brief, Raman spectra acquired at an initial set of random positions inform the next most information-rich location to subsequently sample within the field of view, which in turn iteratively informs the next locations until a stopping criterion associated with the reconstruction error is met. Calculation times on the order of a few milliseconds were insignificant relative to the timeframe for spectral acquisition at a given sampling location. The SLADS approach has the distinct advantage of being directly compatible with standard Raman instrumentation. Furthermore, SLADS is not limited to Raman imaging, providing a time-savings in image reconstruction whenever the single-pixel measurement time is the limiting factor in image generation.