Research Software Engineers (RSEs), students, and researchers operate in a highly demanding environment, often balancing complex coding tasks, diverse project deadlines, and administrative responsibilities. Inefficient time management and poor task prioritization are major obstacles to sustainable research software development and personal productivity. Existing general-purpose task management tools often lack the context-awareness and intelligent automation required to effectively manage the dynamic and specialized workloads typical of RSEs and academic personnel. This work introduces SmartTask, an artificial intelligence-based mobile application specifically designed to address these challenges, leveraging machine learning to promote better work habits and enhance overall efficiency within the RSE community. SmartTask's core innovation lies in its adaptable, personalized scheduling engine. The application utilizes Machine Learning (ML) algorithms to analyze individual work patterns, procrastination tendencies, and historical performance data associated with various types of tasks (coding, writing, meetings). Instead of merely listing deadlines, SmartTask intelligently prioritizes tasks based on a composite score derived from urgency, perceived importance, and the user's optimal working hours, creating truly personalized and actionable schedules. The initial deployment and testing with a pilot group of students and junior RSEs demonstrated a significant potential for improvement in task completion rates and reduced self-reported work-related stress. Preliminary qualitative feedback suggests that users experienced enhanced clarity and reduced decision fatigue when prioritizing tasks. Our key contribution to the deRSE community is demonstrating the practical application of AI/ML in a user-facing, mobile context to address a core non-technical challenge: personal task management.The presentation will detail:1) The architecture of the ML model used for personalized prioritization.2) The API design allows for potential future integration with RSE-specific tools (e.g., Jira, GitLab issues).3) The methodology for future quantitative evaluation of productivity metrics against traditional task management approaches. SmartTask represents a valuable step toward integrating intelligent, user-centric tools into the RSE workflow. We invite the deRSE community to discuss how such AI-driven tools can be formalized and integrated into standard RSE infrastructure.