Corrosion poses a significant challenge to infrastructure integrity, necessitating innovative solutions to predict and mitigate its effects. This study focuses on developing a predictive model for corrosion behavior in infrastructure using non-destructive testing (NDT) data. The proposed model integrates advanced data analytics and machine learning techniques to analyze NDT data collected from infrastructure assets. Key NDT methods considered include ultrasonic testing, radiographic testing, and magnetic particle inspection, which provide critical insights into material degradation without compromising structural integrity. The model leverages historical NDT datasets and incorporates variables such as material composition, environmental conditions, and operational stressors. By employing supervised learning algorithms, the model identifies patterns and predicts corrosion rates, enabling proactive maintenance and extending infrastructure lifespan. The integration of real-time NDT data through IoT-enabled sensors further enhances the model's accuracy, allowing continuous monitoring and timely decision-making. Validation of the predictive model is conducted using case studies from diverse infrastructure types, including pipelines, bridges, and storage tanks. Results demonstrate a strong correlation between model predictions and actual corrosion outcomes, showcasing the model’s reliability in various scenarios. The study emphasizes the importance of feature selection and data preprocessing in improving prediction accuracy. Furthermore, the model is designed to be scalable and adaptable to evolving NDT technologies, ensuring its relevance in future applications. This research contributes to the field by bridging the gap between traditional NDT practices and predictive analytics, offering a cost-effective and sustainable approach to infrastructure management. It highlights the potential of predictive models to reduce maintenance costs, minimize downtime, and enhance safety by anticipating corrosion-related failures.