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Sewerage and stormwater networks are subjected to several deterioration factors, including aging, environmental conditions, and traffic. Maintaining these critical assets in good condition is essential to avoid harmful consequences, such as environmental contamination and negative implications on other infrastructure systems (e.g., water and road networks). Deterioration assessment models are effective and cost-efficient means for proactive management systems that can reduce such consequences. In this connection, this study aims to develop deterioration assessment models for sewer and stormwater pipelines in Hong Kong. First, critical factors that impact the deterioration process of these pipelines were identified. Data for these factors were then collected from the Drainage Services Department (DSD) and open-source data provided by the Hong Kong government. To improve prediction accuracy, a multi-tier concept was utilized in building the models. The first tier categorized pipelines into two groups: fail and not fail, whereas the second tier assigned a grade range from 1 to 3 to the "not fail" pipelines. Several artificial intelligence approaches, such as random forest, neural network, and SVM, were tested. Random forest achieved the highest accuracy in predicting pipelines condition, followed by neural networks. A sensitivity analysis was carried out to investigate the combined impact of two factors, with age being one of them, on the pipeline's performance. The findings of this study provide a robust decision-making tool that DSD authorities and consultants can use to optimize inspection and maintenance activities. • Critical factors impacting pipes deterioration were investigated. • Two models for assessing sewer and stormwater pipes deterioration were created. • A multi-tier concept was integrated in the models to improve prediction accuracy. • Several AI approaches e.g., ANNs, and SVM, were tested in building the models. • Model sensitivity was tested for deterioration factors impact on pipe performance. [ABSTRACT FROM AUTHOR]