Abstract: Transmission Electron Microscopy (TEM) is a crucial technique in materials science, enabling atomic-scale imaging to analyze crystal structures, defects, and material properties. However, atomic-scale TEM data often exhibit high noise levels and multiple imaging modes, making manual analysis highly complex and limiting its applicability in large-scale data processing and automated analysis. In recent years, the rapid advancement of artificial intelligence (AI), particularly deep learning (DL), has provided novel methodologies for the automated and intelligent processing of atomic-resolution TEM data. This review systematically examines the development and recent progress in the automated analysis of atomic-resolution TEM data, with a particular focus on deep learning-based methods for image quality enhancement, precise atomic localization, and characterization analysis. Firstly, the review summarizes the applications of regularization techniques and deep learning-based denoising methods in improving the quality of atomic-scale images. Given the inherent noise in TEM imaging, conventional denoising techniques such as Gaussian filtering and wavelet transformation often struggle to maintain atomic-level details. In contrast, deep learning-based approaches, including convolutional neural networks (CNNs) and transformer-based architectures, have demonstrated superior performance in preserving fine structural information while effectively suppressing noise. Secondly, the review highlights groundbreaking advancements in atomic localization achieved through deep learning. Accurate atomic positioning is fundamental for extracting quantitative structural information, yet conventional image-processing-based approaches face challenges in handling complex imaging conditions. Recent studies have leveraged CNNs, generative adversarial networks (GANs), and self-supervised learning to achieve precise atomic localization even in noisy and low-contrast images. These methods significantly enhance the robustness and accuracy of atomic identification, thereby facilitating the large-scale statistical analysis of atomic structures. Finally, the review explores how AI-driven characterization techniques contribute to the analysis of atomic-scale material structures, defect identification, and phase transition studies. Traditional TEM image analysis often relies on human expertise and heuristic algorithms, which may introduce subjectivity and bias. The integration of AI enables more objective, data-driven approaches to feature extraction, defect recognition, and structural classification. For instance, graph neural networks (GNNs) and reinforcement learning have been applied to infer atomic interactions and dynamic behaviors in TEM datasets, opening new avenues for understanding material properties at the atomic scale. Despite these advancements, several key challenges remain. The quality of training data is a critical issue, as deep learning models require large, high-fidelity datasets for optimal performance. Furthermore, the generalization ability of AI models is still limited when applied to unseen TEM data from different instruments or experimental conditions. Another fundamental challenge lies in the interpretability of deep learning models, as the black-box nature of many architectures hinders their direct application in scientific research. Additionally, integrating physical constraints into AI models remains an open problem, as data-driven approaches often lack explicit consideration of physical principles. Looking ahead, the concept of "intelligent electron microscopy" is expected to shape future developments in this field. The convergence of AI with advanced TEM techniques has the potential to revolutionize atomic-resolution imaging by enabling real-time data processing, adaptive imaging strategies, and fully automated analysis pipelines. By addressing current challenges and further refining deep learning methodologies, AI-powered TEM analysis will play a transformative role in materials science, facilitating the discovery of new materials and the deeper understanding of atomic-scale phenomena.