<p>Smart agriculture requires forecasting tools that remain reliable under resource constraints, because water, fertilizer, energy, and labor pressures make environmental control both an agronomic and an economic problem. This study tests the hypothesis that, for short-horizon temperature–humidity forecasting in a controlled cultivation microclimate, low-complexity linear and regularized linear models can outperform more complex alternatives when the monitored signal is stable and near-linear. Confirming this hypothesis would support lower-cost and more interpretable deployment pathways, whereas rejecting it would justify the additional calibration and computational burden of higher-capacity models. To examine this question, we conducted a comparative evaluation of ten regression families using a continuously collected laboratory microclimate dataset and an external benchmark under a uniform validation framework. Linear and ridge regression achieved the most balanced overall performance, showing that model suitability depends less on algorithmic sophistication than on compatibility with the underlying data regime. The main interdisciplinary contribution of the study is the integration of microclimate sensing, comparative machine-learning evaluation, and deployment-oriented techno-economic reasoning within a single model-selection framework. The main business opportunity lies in low-cost decision-support systems for irrigation timing, microclimate monitoring, and input-use optimization in greenhouse and controlled-cultivation operations, especially in resource-constrained settings. However, the present study should be interpreted as proof of method rather than full commercial validation; financial benefits were not directly quantified and should be established through future field trials that measure installation cost, energy demand, maintenance burden, and savings in water, fertilizer, and labor across crops, climates, and regulatory settings.</p>