Meta-learning, often described as "learning to learn," is a rapidly growing research field in machine learning that focuses on designing models and algorithms capable of quickly adapting to new tasks with minimal data. Unlike traditional approaches that require large datasets and long training times, meta-learning frameworks emphasize task generalization and efficient knowledge transfer. Early research explored optimization-based methods such as Model-Agnostic Meta-Learning (MAML), memory-augmented neural networks, and metric-based methods like Matching Networks and Prototypical Networks. Recent advances integrate meta-learning with deep reinforcement learning, few-shot and zero-shot learning, federated learning, and neural architecture search to handle complex and dynamic environments. Applications span computer vision, natural language processing, robotics, healthcare, and recommendation systems, where data scarcity and task variability are critical challenges. Current research also investigates scaling meta-learning to large models, improving robustness against domain shifts, enabling lifelong and continual learning, and enhancing privacy-aware adaptation. Collectively, these developments establish meta-learning as a key paradigm for building adaptive, efficient, and generalizable AI systems.