<p>The emerging demand of precision-machined components has pushed the medical, aerospace and automotive industries to enhance their focus more on areas of high-quality machining and its predictive modeling, that enhances production efficiency through accurate planning and execution. This study aims to develop a new method based on African vultures optimization algorithm (AVOA) optimized support vector machine (SVM) for optimizing and upgrading the machining performance prediction in turning of aluminum 7068 alloy using a graphene nanoparticle-based coolant. The experiments were conducted using Taguchi L27 orthogonal array for analyzing the impact of key process parameters such as cutting speed, feed rate, and cutting depth, on machining characteristics of surface roughness (Ra) and material removal rate (MRR). The nonlinear correlation between input features and target variables were learned, illustrating the need of developing AVOA-SVM regression models for predicting and analysing machining characteristics. The nonlinear correlation between input features and target variables were learned using Pearson correlation coefficient. The SVM model prediction accuracy was improved through optimizing its hyperparameters employing AVOA algorithm. The efficiency and prediction performance of the proposed methods are verified by experimental trials. The results revealed that the proposed AVOA-SVM regression model can predict surface quality and MRR values accurately and efficiently on partial datasets information. The results were compared with SVM, ANN, DT, AVOA-DT and AVOA-RF models, it was found that AVOA-SVM outperformed in terms of diverse evaluation metrics such as accuracy, RMSE, MAE and MAPE % with greater generalizations. These findings demonstrated improved machining quality and higher MRR, thus providing inspiration for predictive modeling of other machining characteristics along with the intelligent expansion of manufacturing industry.</p>