<p>Efficient machining relies on accurate surface quality prediction to minimize trial and error, enhancing productivity and cost-effectiveness. Despite various methods proposed, research lacks focus on surface quality prediction in abrasive flow machining (AFM) with limited datasets. This study fills this gap by introducing a robust surface quality prediction model, an artificial neural network method based on a small data set, tailored for AFM processes. It reviews existing machine learning methods used in the machining process and their use in the AFM process. The method, including the pre-trained and trained models based on the limited data sample, was established. Corresponding experiments were conducted, and results were compared with predictions, confirming the accuracy and efficiency of the established artificial neural network model using small data sets. The model significantly reduces the need for trial-and-error experiments, thereby improving cost-effectiveness. Additionally, analysis of surface scratches and elemental spectrum elucidates texture enhancement and contamination-free characteristics in the AFM process. This model thus holds promise for optimizing AFM processes, advancing machining efficiency, and reducing costs associated with experimental iterations.&#xa0;</p>