The research was conducted in a greenhouse at the Agricultural Institute Osijek in 2022. The study aimed to evaluate and compare different methods for detecting soil water deficit and plant water‐stress responses in soybean (Glycine max (L.) Merr.). The evaluated methods included soil water content sensors, physiological parameters, and machine learning models. Two irrigation treatments were implemented: optimal soil water content (control treatment, n1, 100 % of field capacity, FC) and water stress (n2, 50% FC) applied during the flowering (f1) and grain-filling (f2) stages. TDR300 and AT sensors exhibited the most rapid response to changes in soil water content (% vol.), indicating high sensitivity to early water deficit. Among physiological indicators, LRCC and RC/CS were the most sensitive during flowering, while PIABS and PITOTAL showed the highest responsiveness during grain filling. The k-Nearest Neighbors (kNN) model demonstrated the highest performance, characterized by high classification accuracy (CA = 0.921) and AUC (0.976). The results emphasize the importance of selecting stage-specific indicators for water stress detection and provide a basis for the development of future integrative monitoring frameworks in soybean production.