Despite significant advancements in the field of medicine, Gastric Cancer (GC) continues to pose a major global health challenge and is currently recognized as the fifth leading cause of cancer-related lethality. The prognosis of the GC is heavily reliant on the staging of the cancer, underscoring the importance of early detection. Traditional diagnostic methods, such as histopathological examination by trained pathologists, are time-consuming and subjective. However, in the rapidly evolving landscape of medical image analysis, cutting-edge technologies like deep learning and computer vision, including techniques such as endoscopy, confocal laser endomicroscopy, magnification endoscopy with narrow-band imaging, chromoendoscopy, and AI-based image analysis, are making significant advancements. These innovations hold great promise for enhancing the accuracy of gastric cancer diagnosis. Nonetheless, a major challenge persists: the scarcity of relevant datasets presents obstacles to computer-aided diagnosis. This review underscores the significance of computer-aided diagnosis in the timely identification and clinical assessment of stomach cancer (SC), focusing on the promising field of histopathological image analysis (HIA) within this broader context. We explore deep-learning techniques, address the challenges associated with dataset scarcity, and discuss potential innovations to overcome this hurdle. Building upon existing research, this review contributes to our understanding of and strategies for improving the detection of gastric cancer, bridging the gap between traditional diagnostic methods and innovative image analysis techniques. This review aims to comprehensively examine the current landscape, highlight significant challenges, conduct a detailed analysis of state-of-the-art technologies, and propose potential directions for future research.