In recent past, artificial neural networks (ANN) have reaped improvements in the domain of medical image processing by addressing many unmanageable problems. The initialized hyperparameters control ANN performance and selecting sensible hyperparameters by hand is time-consuming and tiresome. This study suggests a metaheuristic optimization of the fine-tuning hyperparameters approach to remedy this flaw. The method is then evaluated on mammography images to assess whether the mammogram contains cancer. In the proposed ANN model, a modified Artificial bee colony (ABC) optimization method is used to fine tune the hyperparameters, and it categorizes the tumors in the breast as benign or malignant in two-class case and normal, benign, and malignant in three-class case with an accuracy of 97.52% and 96.58% respectively. Hyperparameters to the neural network framework were assigned instantly with the help of ABC method with wrapped ANN as objective function. Manual search, Grid Search, Random Grid search, Bayes search are all cutting edge ANN hyperparameters methods. In addition to the mentioned, nature-inspired optimization methods such as PSO and GA have adopted for fine tuning parameters. Additionally, the suggested model's performance in classifying breast pictures was compared to that of the published hyperparameter technique using sizable datasets on breast cancer that were made accessible to the public.