Vol. 2, Issue 2, Part A (2025)

The pursuit of generalizable artificial intelligence (AI) has driven increasing attention toward meta-learning an approach that enables systems to “learn how to learn” and adapt efficiently across diverse tasks with minimal data. This study investigates a comprehensive meta-learning framework integrating domain diversification, uncertainty modeling, and curriculum-based task sampling to enhance cross-domain generalization. Using benchmark datasets from vision, natural language processing, and reinforcement learning, the proposed model was compared against established meta-learning baselines, including Model-Agnostic Meta-Learning (MAML), Prototypical Networks, and Latent Embedding Optimization (LEO). Quantitative evaluations demonstrated that the proposed approach consistently achieved higher accuracy, faster adaptation speed, and improved generalization on unseen domains. Statistical analysis confirmed the significance of these improvements (p≤0.01), validating the hypothesis that embedding domain diversity and uncertainty estimation within the meta-optimization loop strengthens learning stability and transferability. Furthermore, ablation experiments revealed that the combination of all three mechanisms domain regularization, uncertainty modeling, and task diversity was essential for maximizing performance. The results provide new insights into how meta-learning can move beyond rapid adaptation toward true generalization, bridging the gap between narrow task performance and human-like flexibility. The study concludes that this integrative framework offers a viable pathway for developing AI systems capable of continual learning and dynamic adaptation in nonstationary, real-world environments. Practical recommendations emphasize the adoption of domain-aware meta-training strategies and uncertainty-aware evaluation protocols for AI deployment in healthcare, robotics, and autonomous systems. Ultimately, this research highlights meta-learning as a cornerstone for constructing next-generation AI models that are robust, interpretable, and capable of lifelong generalization.