The early prediction of depressive mood episodes is crucial for effective intervention in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD). This study explores a predictive framework leveraging digital phenotypic data collected from smartphones and smartwatches, with a focus on circadian rhythm indicators such as Dim Light Melatonin Onset (DLMO). Using data from 164 participants within the Mood Disorder Cohort Research Consortium in Korea, time-series features related to sleep, heart rate, activity levels, and light exposure were processed to predict mood episodes seven days in advance. Deep learning models, including LSTM, GRU, and an LSTM-GRU hybrid, were applied to analyze this data, with the GRU model achieving the highest recall (0.767) and the LSTM model displaying superior robustness across metrics. SHAP value analysis of DLMO-related variables further underscored the association between circadian rhythm disruptions and depressive episodes, with delayed wake-up times relative to ideal schedules linked to increased depressive symptoms. Our findings demonstrate the feasibility of using digital phenotypes for early detection of mood episodes. These results highlight the potential of automated monitoring systems in clinical practice, which enable proactive intervention strategies through continuous, objective monitoring of patient conditions.