Helicobacter pylori (H. pylori) infection is a well-established contributor to the onset and progression of gastric cancer (GC), with global prevalence affecting nearly half of the population. Conventional diagnostic techniques ranging from invasive endoscopy and biopsy to non-invasive tests such as urea breath and stool antigen assays are often constrained by limitations in accuracy, invasiveness, and effectiveness in early-stage cancer detection. In recent years, artificial intelligence (AI) has emerged as a powerful tool in enhancing diagnostic capabilities for H. pylori-associated gastric malignancies. This review critically examines the application of AI, particularly machine learning and deep learning strategies, to endoscopic image analysis. Techniques such as convolutional neural networks (CNNs), transfer learning, and model ensembles have yielded notable improvements in diagnostic performance, demonstrating high levels of sensitivity and specificity. Furthermore, AI algorithms have shown potential in early lesion detection and precise classification of H. pylori infection status. Despite these advancements, key challenges remain, including data scarcity, model transparency, and integration into clinical workflows. Looking ahead, the development of multimodal AI systems that fuse imaging, clinical parameters, and genomic data, alongside personalized risk models, may significantly enhance diagnostic precision. Ultimately, AI holds significant promise in revolutionizing the early and accurate diagnosis of H. pylori-related gastric cancer, with implications for improved clinical outcomes and population-level screening programs.