Mental health disorders—including depression, anxiety, trauma-related, and psychotic conditions—are pervasive and impairing, representing considerable challenges for both individual well-being and public health. Often the first challenge to treatment can be financial, geographic, and stigmatic barriers which limit the accessibility of traditional assessment measures. Further, compounded by frequent misdiagnosis or delayed detection, there is a need for effective, accessible, and scalable approaches to identification and management. Leveraging advances in computing and the ubiquitous nature of personal mobile and wearable technology, this narrative review examines the utilization of passive sensor data as a screening and diagnostic tool for mental disorders. As an alternative to traditional screening measures, passive sensing offers a tool to overcome barriers which prevent many from seeking services. We critically assess the literature up to September 2023, exploring the use of passive data—such as heart rate variability, movement patterns, and geolocation—to predict mental health outcomes across a spectrum of disorders. Through a translational perspective, our review explores the state of passive sensing science, with special emphasis on the capacity for the science to be implemented in real-world clinical and general populations. To this aim, we consider study designs, including participant demographics, data collection methods, sensor modalities, outcome measures, and analytic modeling approaches. Our findings highlight overarching limitations in the field including 1) the use of smaller, specialized sample populations, and 2) the predominant use of Android operating systems, 3) a reliance on self-reported measures as proxies for mental health outcomes which ultimately limit the potential to provide robust mental health assessment in larger population samples. We suggest that further research incorporate larger and more diverse samples, inclusion of further smartphone operating systems, and additional clinical-related assessments to strengthen predictive models and maintain a clinician in the loop. Despite these limitations, passive sensing technologies like GPS, heart rate monitoring, and actigraphy offer promise for enhancing early detection and improving the diagnostic process for mental disorders. We conclude that careful consideration of translational factors in the design of future research will aid in enhancing the potential and scope of future passive sensing studies, ultimately enhancing mental health outcomes on a broad scale.