Background: Acute appendicitis is one of the most quotidian surgical emergencies worldwide, accounting for a large share of emergency admissions. Despite modern lab tests and imaging, diagnosis often remains uncertain, leading to complications, delays, or unnecessary surgery. Emerging tools like machine learning and deep learning are helping improve accuracy and decision-making. Objective: This review explores how artificial intelligence (AI) including machine learning and deep learning, is being used in diagnosing and managing acute appendicitis. Evidence shows AI models often outperform traditional scoring systems like Alvarado and AIR (Appendicitis Inflammatory Response), offering greater accuracy, fewer unnecessary surgeries, and stronger support for clinical decision-making and future integration. Methods: This narrative review was conducted to synthesize current evidence on the role of artificial intelligence in acute appendicitis. A structured literature search was performed using Google Scholar, PubMed, Scopus, and Web of Science, covering publications from January 2010 to December 2025. Case reports, editorials, and non‑English language articles were excluded. Results: AI can now read computed tomography (CT) scans as effective as radiologists and tell simple from complex appendicitis. In children, using AI with ultrasound can reduce the need for CT scans. Research suggests AI can also predict perforation risk, outcomes, and who might be managed without surgery. But before AI is widely used, it still needs strong testing, smooth fit into daily practice, and methodical ethical reviews. Conclusion: AI shows great potential in the management of patients with appendicitis, but robust multicenter trials and integration into surgical workflows are needed before widespread adoption.