The integration of Artificial Intelligence (AI) into primary education curriculum governance remains constrained by conceptual ambiguity, ethical concerns, and digital inequalities, particularly in under-resourced education systems where technological readiness is limited. This study addresses these challenges by conducting a systematic literature review to explore how AI can be ethically, effectively, and contextually embedded into curriculum decision-making. Grounded in five theoretical frameworks—Data-Driven Decision Making, Adaptive Learning, AI-Based Decision Support Systems, Contextual Curriculum Design, and Technology Ethics in Education—the review synthesizes findings from peer-reviewed publications over the past decade. Results reveal that AI holds significant potential to strengthen curriculum planning through real-time assessment, personalized learning trajectories, and prescriptive analytics that enhance evidence-based decisions. Nevertheless, systemic barriers such as poor digital infrastructure, limited AI literacy among educators, and fragmented policy directions continue to hinder large-scale adoption and sustainability. To respond to these challenges, the study proposes an integrative conceptual model that repositions AI not merely as a technological tool but as an ethically grounded and contextually adaptive agent within curriculum governance. Such a model emphasizes that the meaningful and equitable application of AI requires strong cross-sectoral collaboration, coherent policy alignment, and sustained capacity-building initiatives. By advancing this perspective, the study underscores the importance of positioning AI as a catalyst for inclusive and transformative educational change, ensuring that technological innovation aligns with ethical imperatives and local contextual needs.