Manic episodes in bipolar disorder are characterized by acute behavioral escalation requiring early intervention. This research proposes a multimodal digital phenotyping framework integrating keystroke dynamics with circadian rhythm features to forecast manic episodes 3-7 days prior to clinical onset. The system leverages a hybrid architecture of temporal convolutional and recurrent neural networks with personalized adaptation. It generates risk predictions and clinically actionable alerts while ensuring user privacy through strict on-device processing and data encapsulation. This framework addresses a critical gap in mental health-care: providing passive, unobtrusive monitoring to detect pre-onset behavioral signatures within a clinically actionable window.