Bipolar disorder is identified by intense shifts in mood swings that can mainly hinder day-to-day activities, making accurate prognosis and timely mitigating important for effective management. This study introduces a novel machine learning strategy that harnesses real-time smartphone data to forecast episodes of bipolar disorder. The presented machine learning model analyzes the behavioral pattern along with communication habits, mobility, and app usage, that allows for the early-stage detection of the signs of mood cycles, facilitating early detection and intervention. This real-time monitoring of data provides a more concise and timely approach to intervene at an early-stage detection in comparison to traditional methods that often depend on recall-based assessments. This model takes multiple data sources, including environmental impacts, genetic information, and clinical assessments, to reduce error in models. The predictive models will be further improved by complex machine learning techniques—such as supervised learning, ensemble methods, and feature engineering. The research work attempts to generate personalized interventional strategies which can be cross-validated with the external dataset for improving the quality of life in patients with bipolar disorder by reducing the frequency and severity of episodes.