The number of people diagnosed with cancer is rising worldwide and has become a leading cause of death. Therefore, it is essential to perform early diagnosis effectively to improve the success of treatment and increase survival rates. Currently, histopathological examination is the main method for confirming a cancer diagnosis. However, this analysis depends on the pathologist's skill in interpreting tissue specimens, which can be time-consuming and varies among different pathologists. Recent developments in digital pathology and artificial intelligence have introduced Computer-Aided Diagnosis (CAD) systems. These systems can automate cancer detection through histopathological image analysis. In this review, we summarize the advances made from 2020 to 2025 in AI-assisted histopathological image analysis. The main topics discussed include available public datasets for AI-assisted histopathological image analysis, one or more image preprocessing methods, machine learning techniques, deep learning methods, the use of Vision Transformers and Explainable Artificial Intelligence (XAI), the development of foundation models, and a comparison of the performance between deep learning and traditional machine learning methods, including differences in accuracy and robustness. While CAD systems have made significant strides, several challenges remain before successful clinical application. These include issues related to dataset variability, interpretability, and clinical deployment. Ongoing research aims to enhance the understanding of foundation models, federated learning, and multimodal AI systems in precision oncology. Key Terms: Histopathology,Cancer Detection, Deep Learning,Digital Pathology, Vision Transformers, Explainable AI.