In order to comprehensively investigate the spatio-temporal dynamics of corrosion evolution under complex pipeline environments and improve the corrosion rate prediction accuracy, a novel framework for corrosion rate prediction based on adaptive multivariate time series graph neural network (MTGNN) multi-feature spatio-temporal correlation analysis is proposed. First, pipeline monitoring points are modeled as graph nodes to construct the pipeline corrosion spatio-temporal information graph, with corrosion rate and auxiliary features (selected through feature correlation analysis) forming node attributes. Then, a dynamic adjacency matrix is adaptively learned to capture hidden spatial dependencies, while temporal convolution modules extract multi-scale temporal patterns, and the node sequences with integrated corrosion features are input into the adaptive MTGNN for prediction. To reduce the accumulation of errors in multi-step prediction, a “chunked progressive” training strategy is adopted, incrementally expanding prediction horizons. Finally, experiments based on real urban drainage pipeline data show that in six-step predictions, the model reduces MAE, RMSE, and MAPE by 6.59–32.16%, 4.38–27.95%, and 5.01–22.22%, respectively, compared to traditional time series methods such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and non-adaptive MTGNN. The results indicate that the adaptive MTGNN, which integrates multi-source node features, has higher prediction accuracy across the three evaluation metrics, highlighting its capability to leverage spatio-temporal synergies for accurate short-term corrosion rate prediction.