This paper aims to investigate the application of artificial intelligence in robotic arm automation control for the accurate identification of different types of garbage using deep learning algorithms. The goal is to enable the robotic arm to autonomously classify and handle garbage. The appropriate garbage classification dataset was selected and subjected to data preprocessing in this study. After comparing various well-established convolutional neural network models, including VGG16, InceptionResNetV2, Xception, and InceptionResNetV3, in terms of performance and suitability on the target dataset, the Xception model, which exhibited the best performance metrics, was selected for this research. Subsequently, the paper optimized the model by incorporating self-attention mechanisms, self-optimization strategies for learning rate, learning weight adjustments, and unfreezing of pre-trained layers, resulting in a predictive accuracy of 96.9% on the test set and an AUC area of 0.9989. Additionally, the paper simulated the robotic arm in a simulated environment and successfully achieved the objective of automatic garbage identification and classification using the developed model.