Few-shot learning is essential for tasks involving limited labeled data, where traditional deep learning models often face challenges. This paper provides a comprehensive comparative analysis of four prominent few-shot learning techniques: Prototypical Networks, Siamese Networks, Relation Networks, and Matching Networks. These methods are evaluated using key performance metrics, including accuracy, inference time, and GFLOPS, to determine their suitability for various application scenarios. Our experimental results show that Prototypical Networks achieve the highest classification accuracy of 90.38%, making them ideal for applications such as medical image diagnosis and anomaly detection in industrial settings. Siamese Networks excel in inference speed and computational efficiency, making them well-suited for low-resource environments, such as facial recognition on mobile devices. While Relation Networks require higher computational resources, they offer competitive accuracy and can be utilized in e-commerce for personalized product recommendations. Matching Networks provide balanced performance, making them effective for dynamic environments like real-time inventory tracking in retail. However, challenges remain, including limited dataset diversity impacting generalization, scalability issues with computationally intensive methods, and difficulties in handling intra-class variability and noisy data. Future work will explore advanced optimization techniques, such as quantization and pruning, to further reduce computational overhead and improve deployment feasibility on edge devices. Additionally, efforts will focus on extending the application of these models to real-world challenges, including wildlife conservation through species identification and disaster response via rapid visual data analysis.