Objective: Digital stress interventions could be helpful as an “indirect” treatment for depression, but it remains unclear for whom this is a viable option. In this study, we developed models predicting individualized benefits of a digital stress intervention on depressive symptoms at 6-month follow-up. Method: Data of N = 1,525 patients with depressive symptoms (Center for Epidemiological Studies’ Depression Scale, CES-D ≥ 16) from k = 6 randomized trials (digital stress intervention vs. waitlist) were collected. Prognostic models were developed using multilevel least absolute shrinkage and selection operator and boosting algorithms, and were validated using bootstrap bias correction and internal–external cross-validation. Subsequently, expected effects among those with and without a treatment recommendation were estimated based on clinically derived treatment assignment cut points. Results: Performances ranged from R² = 21.0%–23.4%, decreasing only slightly after model optimism correction (R² = 17.0%–19.6%). Predictions were greatly improved by including an interim assessment of depressive symptoms (optimism-corrected R² = 32.6%–35.6%). Using a minimally important difference of d = −0.24 as assignment cut point, approximately 84.6%–93.3% of patients are helped by this type of intervention, while the remaining 6.7%–15.4% would experience clinically negligible benefits (δˆ = −0.02 to −0.19). Using reliable change as cut point, a smaller subset (39.3%–46.2%) with substantial expected benefits (δˆ = −0.68) receives a treatment recommendation. Conclusions: Meta-analytic prognostic models applied to individual participant data can be used to predict differential benefits of a digital stress intervention as an indirect treatment for depression. While most patients seem to benefit, the developed models could be helpful as a screening tool to identify those for whom a more intensive depression treatment is needed.