The integration of behavioral sensing and Artificial Intelligence (AI) has increasingly proven invaluable across various domains, offering profound insights into human behavior, enhancing mental health monitoring, and optimizing workplace productivity. This thesis presents five pivotal studies that employ smartphone, wearable, and laptop-based sensing to explore and push the boundaries of what these technologies can achieve in real-world settings. This body of work explores the innovative and practical applications of AI and behavioral sensing to capture and analyze data for diverse purposes. The first part of the thesis comprises longitudinal studies on behavioral sensing, providing a detailed, long-term view of how significant events, such as the COVID-19 pandemic and professional promotions, impact mental health and productivity among college students and information workers. The "College Experience Study" spans five years, tracking two cohorts of students through their entire college experience, offering a rare longitudinal perspective on student behavior and mental health during global disruptions. The second study provides a year-long observation of information workers, focusing on physiological and behavioral changes post-promotion, highlighting how career advancements influence personal well-being and professional duty. The second part of the thesis expands the application of behavioral sensing with novel methods and integration with AI technologies. This section introduces a pioneering approach to mental health assessment through the "MoodCapture Study" which utilizes passively captured smartphone images to detect depression symptoms. Additionally, we explore the enhancement of personal productivity and well-being through AI-driven tools, including Large Language Models (LLM) powered personalized productivity agents and the MindScape mobile application for contextual AI-driven journaling. These tools leverage real-time behavioral data to provide adaptive, user-specific support, demonstrating the effectiveness of integrating advanced AI with behavioral sensing in creating responsive and user-centric applications. Overall, our work highlights significant advancements in AI and behavioral sensing, providing practical applications and implications for improving mental health, enhancing educational experiences, and boosting workplace productivity. Each study contributes to a broader understanding of the dynamic interaction between technology and human behavior, laying the groundwork for future innovations that could lead to more efficient, healthy, and productive lifestyles. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]