Artificial Intelligence (AI) incorporated into the education domain lead to transformational possibilities for personalized, adaptive, and competency-based education. Concurrently, the Outcomes-Based Education (OBE) model emerged, giving rise to strategic approaches based on defined learning outcomes, learner-cantered pedagogical practice, and the ongoing assessment of performance. The incorporation of AI in education has reinvented pedagogical frameworks, especially when conjoined with OBE. This review articulates the way in which AI technologies Deep Learning (DL), Reinforcement Learning (RL), Personalized Learning (PL), Natural Language Processing (NLP), Gamification, and Learning Analytics align with educational outcomes in cognitive and affective domains. Higher education institutions utilizing AI augmented OBE frameworks develop learner centered environments, personalize learner learning pathways, promote appropriate summative assessment strategies, adopt ethical competencies, and apply problem-solving skills, understanding that these elements are needed for a workforce competent in AI. The paper identifies current limitations including scale, issues with standardization, and the absence of comprehensive theoretical models or articulation linking AI with OBE practices in education. In summary, the review highlights the potential for a shift to AI based OBE and potential deficiencies, and subsequent recommendations for effective and ethical AI in education.