Meta-learning in neural networks is a rapidly growing field that aims to improve learning algorithms by leveraging experience from multiple learning episodes. Unlike conventional AI approaches that solve tasks from scratch, meta-learning focuses on enhancing the learning process itself, addressing challenges such as data and computation bottlenecks, and improving generalization. This survey provides a comprehensive overview of the current state of meta-learning, including its definitions, relationships with related fields like transfer learning and hyperparameter optimization, and a new taxonomy for categorizing meta-learning methods. It also discusses promising applications such as few-shot learning and reinforcement learning, as well as outstanding challenges and future research directions. The paper introduces a new taxonomy based on three axes: meta-representation (what to learn), meta-optimizer (how to learn it), and meta-objective (why to learn it). This framework helps in developing new meta-learning methods and customizing them for different applications. The survey covers various methodologies, including parameter initialization, optimizer learning, feed-forward models, embedding functions, loss learning, architecture discovery, and hyperparameter optimization. It also discusses the relationship between meta-learning and related fields such as transfer learning, domain adaptation, continual learning, and multi-task learning. The paper highlights the importance of meta-learning in improving data efficiency, knowledge transfer, and unsupervised learning. It also addresses challenges such as meta-overfitting, the need for efficient optimization strategies, and the scalability of meta-learning methods. The survey concludes by emphasizing the potential of meta-learning to advance the frontier of deep learning and its applications in various domains, including reinforcement learning, neural architecture search, and data-efficient learning.Meta-learning in neural networks is a rapidly growing field that aims to improve learning algorithms by leveraging experience from multiple learning episodes. Unlike conventional AI approaches that solve tasks from scratch, meta-learning focuses on enhancing the learning process itself, addressing challenges such as data and computation bottlenecks, and improving generalization. This survey provides a comprehensive overview of the current state of meta-learning, including its definitions, relationships with related fields like transfer learning and hyperparameter optimization, and a new taxonomy for categorizing meta-learning methods. It also discusses promising applications such as few-shot learning and reinforcement learning, as well as outstanding challenges and future research directions. The paper introduces a new taxonomy based on three axes: meta-representation (what to learn), meta-optimizer (how to learn it), and meta-objective (why to learn it). This framework helps in developing new meta-learning methods and customizing them for different applications. The survey covers various methodologies, including parameter initialization, optimizer learning, feed-forward models, embedding functions, loss learning, architecture discovery, and hyperparameter optimization. It also discusses the relationship between meta-learning and related fields such as transfer learning, domain adaptation, continual learning, and multi-task learning. The paper highlights the importance of meta-learning in improving data efficiency, knowledge transfer, and unsupervised learning. It also addresses challenges such as meta-overfitting, the need for efficient optimization strategies, and the scalability of meta-learning methods. The survey concludes by emphasizing the potential of meta-learning to advance the frontier of deep learning and its applications in various domains, including reinforcement learning, neural architecture search, and data-efficient learning.