The accurate acquisition of underwater topographic data is crucial for the representation of river morphology and early warning of water hazards. Owing to the complexity of the underwater environment, there are inevitably outliers in monitoring data, which objectively reduce the accuracy of the data; therefore, anomalous data detection and processing are key in effectively using data. To address anomaly detection in underwater terrain data, this paper presents an optimised DBSCAN-IForest algorithm model, which adopts a distributed computation strategy. First, the K-distance graph and Kd-tree methods are combined to determine the key computational parameters of the DBSCAN algorithm, and the DBSCAN algorithm is applied to perform preliminary cluster screening of underwater terrain data. The isolated forest algorithm is subsequently used to carry out refined secondary detection of outliers in multiple subclusters that were initially screened. Finally, the algorithm performance is verified through example calculations using a dataset of about 8500 underwater topographic points collected from the Yellow River Basin, which includes both elevation and spatial distribution attributes; the results show that compared with other methods, the algorithm has greater efficiency in outlier detection, with a detection rate of up to 93.75%, and the parameter settings are more scientifically sound and reasonable. This research provides a promising framework for anomaly detection in underwater terrain data.