The composting of grape pomace represents an environmentally sustainable method for valorizing agro-industrial waste by converting it into nutrient-rich organic fertilizer. This study aimed to develop robust predictive models for monitoring microbial population dynamics during composting using near-infrared (NIR) spectroscopy as a rapid, non-destructive analytical tool. A total of nine composting experiments were carried out in laboratory-scale reactors (V = 5 L) over a 30-day period, with the initial substrate moisture content ranging from 50% to 65% and the air flow rates ranging from 0.35 to 2.00 L/min. NIR spectra were recorded within the 904–1699 nm range using an NIR-128-1.7-USB/6.25/50 μm spectrometer. Artificial Neural Network (ANN) models were developed to correlate spectral data with microbiological parameters, including bacterial, fungal, and total microorganism counts. Among various spectral preprocessing techniques, smoothing was identified as the most effective for predicting bacterial counts, yielding the highest Residual-Error-of-Prediction-to-Standard-Deviation ratio (RER = 10.083). In contrast, the combination of the second-order Savitzky–Golay derivative followed by multiplicative scatter correction (SG2D+MSC) proved optimal for modeling fungal (RER = 15.075) and total microorganism counts (RER = 12.040). The use of NIR spectroscopy offers several advantages, including minimal sample preparation, rapid data acquisition, the ability to analyze samples in situ, and simultaneous monitoring of multiple compost parameters. Combined with ANN modeling, this approach enables real-time, accurate prediction of compost microbiology, offering significant potential for process optimization. These findings demonstrate the value of integrating NIR and machine learning tools in advancing sustainable and efficient composting practices.