Cystic fibrosis (CF) is a genetic disorder that primarily affects the respiratory, digestive, and reproductive systems. In the United States, approximately 32,000 individuals, spanning both children and adults, suffer from CF, and roughly 1,000 new cases are diagnosed annually. The current gold standard for CF diagnosis is the sweat test, yet this method is plagued by issues such as being timeconsuming, expensive, challenging to replicate, and lacking treatment monitoring capabilities. In contrast, the emerging field of wearable sweat biosensors has gained significant attention due to their potential for noninvasive health monitoring. Despite this, there remains a conspicuous absence of a wearable sweat biosensor tailored specifically for CF diagnosis and monitoring. Here, this study introduces a flexible wearable sweat biosensor, named CFTrack, designed to address the unique challenges associated with CF. This proposed CFTrack biosensor not only facilitates CF diagnosis but also enables the monitoring of medication treatment effectiveness and tracks therapy activities. In addition, it operates in a self-powered and customized manner, ensuring seamless integration into the daily lives of individuals with CF. Given that sweat tests and fitness routines are the predominant methods for diagnosing and treating cystic fibrosis patients, respectively, the proposed CFTrack biosensor leverages ion concentration in sweat for diagnostic purposes. Additionally, it incorporates a motion-tracking function to monitor physical activity, providing a comprehensive approach to CF management. To evaluate the feasibility of the proposed CFTrack biosensor, a comprehensive evaluation has been performed including numerical simulations, theoretical analyses, and experimental tests. The results demonstrate the efficacy of the proposed CFTrack biosensor in diagnosing and monitoring CF conditions while also showcasing its ability to effectively track the progress of patients undergoing physical therapy. The proposed CFTrack biosensor resolves key issues associated with existing sweat sensors including high energy consumption, intricate fabrication procedures, and the absence of continuous monitoring capabilities. By addressing these challenges, the proposed sweat biosensor aims to revolutionize CF diagnosis and monitoring, offering a more efficient and user-friendly alternative to current methods.