This study presents a comparative evaluation of multiple-approach optical spectroscopic sensor—Laser-Induced Breakdown Spectroscopy (LIBS), Mid-Infrared Spectroscopic sensing (MIRS), and Hyperspectral Imaging (HSI)-based sensors operating in the Visible–Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) ranges—for the quantitative detection of calcium carbonate (CaCO₃) in pelletized CaCO₃-CaO mixtures. The objective was to assess and compare the sensing performance of these optical sensor platforms for carbonate quantification. Each spectroscopic sensor dataset was processed using chemometric calibration methods, including Partial Least Squares Regression (PLSR), to ensure robust and reproducible quantitative predictions. Although the samples consisted of binary CaCO₃-CaO mixtures, the sensing task focused exclusively on CaCO₃ content. Results indicate that LIBS, MIRS, and HSI-SWIR-based sensing approaches achieved comparable quantitative performance, with LIBS providing the highest prediction accuracy. In contrast, the HSI-VNIR sensor configuration demonstrated lower predictive capability relative to the other optical sensing modalities. These findings highlight the potential and limitations of different optical sensor technologies for carbonate detection in heterogeneous mineral systems.