Climate and husbandry-induced stressors pose significant threats to fish growth and survival. Therefore, identifying reliable biomarkers is crucial for mitigating stress and enhancing fish health. This study conducted a comprehensive transcriptomic analysis of stress responses in rainbow trout exposed to five different stress conditions—high and low temperatures, crowding, salinity, and low-quality water—for six hours. In total, 21,580 differentially expressed transcripts (DETs) were identified, with 16,959 being unique DETs. Among the conditions studied, heat stress and salinity triggered the most significant transcriptomic responses. Most DETs were specific to individual stressors, indicating distinct physiological responses. Only 39 DETs were commonly regulated across all conditions. The most significant unique DETs associated with heat stress were utilized in machine learning analyses to assess their effectiveness in distinguishing between control and heat-stressed fish from natural Redband trout populations. The logistic model tree (LMT) performed best using a set of 234 DETs. When the dataset was reduced to 50 or 2 DETs, the Random Forest model achieved optimal classification at several time points. Notably, the model consistently relied on two heat shock genes, hsp47, and HSPA4L, as key predictors across all time points (both short- and long-term stress) as well as the combined dataset. In contrast, core DETs (shared between conditions) were less effective in predicting phenotypes, achieving only 52.78% accuracy. The study concluded that molecular signatures are largely specific to individual stressors. It identified potential biomarkers for monitoring stress associated with climate change and recommended their application in breeding programs to enhance fish welfare, improve aquaculture productivity, and support species conservation efforts.