<p>Drought stress hinders plant development by limiting water availability, disrupting osmotic equilibrium, and impairing nutrient uptake and photosynthetic efficiency. To counter these effects, temporary immersion bioreactors (TIBs) provide a scalable platform for rapid shoot proliferation and in vitro screening of sugarcane genotypes subjected to mannitol-induced osmotic stress. These controlled responses often parallel field performance, enabling efficient early-stage selection. In this study, apical meristems of sugarcane (cultivar C-1051-73) were cultured in vitro, transferred to TIBs, and exposed to mannitol concentrations ranging from 0 to 200 mM. Key physiological parameters and biochemical stress markers were quantified. Discriminant analysis, implemented via Python-based machine learning, classified stress responses using Fisher’s linear function. Control (0 mM) and high-stress (200 mM) treatments served as training sets, while intermediate concentrations (50–150 mM) were used for model validation. The analysis identified 150 mM as the threshold for stress manifestation, with lower concentrations classified as non-stressed. This analytical framework strengthens genetic improvement strategies—including selection, hybridization, mutagenesis, and transgenesis—by enabling early identification of drought-tolerant genotypes prior to field deployment. The approach reduces costs, shortens breeding cycles, and enhances decision-making in cultivar development. Moreover, the methodology is transferable to other crops, provided that discriminant functions are recalibrated to accommodate species-specific physiological profiles. Altogether, the integration of TIBs with discriminant analysis offers a robust, cost-efficient solution for drought tolerance screening, advancing precision agriculture and promoting crop resilience under water-limited conditions.</p>