Wearable technology, particularly devices like Fitbit, offers a wealth of continuous health and fitness data that can be leveraged for predictive analysis. This paper explores the use of machine learning techniques to analyze and predict trends from Fitbit-collected data, focusing on key metrics such as heart rate, physical activity, sleep patterns, and calorie consumption. We use a large dataset of activity of different types of people over a one-month period. By applying predictive models to these metrics, we identified patterns in user health behavior and forecasted future trends in physical activity and overall well-being. The findings highlight the potential of wearable devices for real-time health monitoring and proactive health management, showcasing how data-driven insights can enhance personalized health outcomes. Our results demonstrate the effectiveness of predictive analytics in wearable health data and suggest pathways for future research in personalized healthcare solutions.