The rapid advancement of Industrial Internet of Things (IIoT) and machine learning technologies has exposed limitations in traditional corrosion risk assessment methods, particularly in terms of accuracy and real-time performance. This study presents an innovative machine learning-based framework for corrosion risk assessment in long-distance pipelines. The framework integrates public environmental factors (e.g., meteorological and geological data) with private operation and maintenance data (e.g., intelligent pigging and cathodic protection monitoring data). For data management, customized preprocessing workflows have been designed for various data types, and information is organized in an N-dimensional vector format to ensure data quality and consistency. The modeling component employs an adaptive optimization algorithm based on historical data, incorporating multiple pre-set machine learning models and their hyperparameter spaces. Through automatic adjustment and selection of optimal model configurations, the framework significantly improves the accuracy and generalization capability of risk assessment. Multiple specialized models are coupled using ensemble learning methods, forming an end-to-end risk assessment workflow. This study also explores strategies for model deployment and continuous optimization mechanisms, ensuring the framework’s scalability and maintainability in practical production environments. The proposed comprehensive framework aims to enhance the accuracy, efficiency, and adaptability of corrosion risk assessment for long-distance pipelines, thereby providing robust support for operation and maintenance decisions.