The capability of models to more quickly adapt to new tasks based on limited data is what has made meta-learning (or learning to learn), one of the promising research areas in artificial intelligence. The current paper discusses the literature in detail on meta-learning methodologies and describes the three broad approaches, including model-based, metric-based, and optimization-based meta-learning strategies. It will give a summary of the new state-of-the-art methods and review their advantages and disadvantages, and a variety of applications, including computer vision, natural language processing, and reinforcement learning. There is a description of the differences between performance, the challenges, and research gaps carried out with the help of comparative analysis of chosen studies. Another direction of future research suggested in this review is the one that combines the principles of meta-learning in predicting software refactoring so that they could support a more sufficient adaptation and automation of large-scale software systems. The insights that are given are expected to inform researchers to create systems that are more effective and robust in terms of meta-learning.