<p>Clinical symptoms in serious mental illness (SMI) fluctuate dynamically, yet standard interview-based assessments are limited in tracking these changes. Smartphone-based ecological surveys offer a scalable approach to monitoring symptoms in naturalistic settings. We analyzed intensive longitudinal data from 56 outpatients with psychotic or affective disorders who contributed 3901 daily surveys and 423 clinical assessments over one year or longer. The daily survey items assessed a broad range of general affective, behavioral, and functional experiences. Using participant-level leave-one-subject-out cross-validation, we trained machine learning models to estimate concurrent clinical symptom severity from smartphone daily surveys. Models showed significant predictive performance for the Montgomery–Åsberg Depression Rating Scale (repeated measures correlation [r_rm] = 0.57; p &lt; 0.001) and Young Mania Rating Scale (r_rm = 0.39; p &lt; 0.001). Estimating positive symptoms measured by the Positive and Negative Syndrome Scale also showed significant associations (r_rm = 0.24, p &lt; 0.001), but with variable accuracy across participants. Factor modeling demonstrated the strongest convergent validity for negative affective domains, indicating that survey responses aligned most closely with depression, anxiety, and irritability. Importantly, machine learning estimated MADRS scores tracked within-person symptom fluctuations, demonstrating sensitivity to clinically meaningful changes over time. Adherence decreased over time but was not significantly associated with symptom severity. These findings demonstrate that smartphone-based daily surveys can generate concurrent estimates of negative affective symptom severity in real-world contexts, supporting a scalable framework for extending symptom assessment beyond in-person clinical settings in psychotic and affective disorders.</p>