The uses of technology-delivered mental health treatment options, such as interventions delivered via computer, smart phone, or other communication or information devices, as opposed to primarily face-to-face interventions, are proliferating. However, the literature is unclear about their effectiveness as preventive interventions for higher education students, a population for whom technology-delivered interventions (TDIs) might be particularly fitting and beneficial. This meta-analytic review examines technological mental health prevention programs targeting higher education students either without any presenting problems (universal prevention) or with mild to moderate subclinical problems (indicated prevention). A systematic literature search identified 22 universal and 26 indicated controlled interventions, both published and unpublished, involving 4763 college, graduate, or professional students. As hypothesized, the overall mean effect sizes (ESs) for both universal (0.19) and indicated interventions (0.37) were statistically significant and differed significantly from each other favoring indicated interventions. Skill-training interventions, both universal (0.21) and indicated (0.31), were significant, whereas non-skill-training interventions were only significant among indicated (0.25) programs. For indicated interventions, better outcomes were obtained in those cases in which participants had access to support during the course of the intervention, either in person or through technology (e.g., email, online contact). The positive findings for both universal and indicated prevention are qualified by limitations of the current literature. To improve experimental rigor, future research should provide detailed information on the level of achieved implementation, describe participant characteristics and intervention content, explore the impact of potential moderators and mechanisms of success, collect post-intervention and follow-up data regardless of intervention completion, and use analysis strategies that allow for inclusion of cases with partially missing data.