Indian Sign Language (ISL) recognition plays an important role in making seamless communication possible for the hearing and speech-impaired community, enabling accessibility in education, workplaces, and daily interactions. However, the scarcity of annotated datasets causes a significant challenge for traditional machine learning methods, which struggle to perform well with limited labeled data. Currently, labeled data is primarily available for numbers and alphabets, which are not particularly useful for recognizing the full range of ISL gestures, further exaggerating the lack of labeled data. Few-shot learning offers a promising solution by enabling models to generalize effectively with limited labeled examples. Meta Learning has proven to be a promising approach to addressing this challenge by training models to adapt quickly to new tasks with minimal data. In this paper, we perform a comparative analysis of five prominent few-shot learning methods: Model-Agnostic Meta-Learning (MAML), Reptile Meta Learning, Prototypical Networks, Relation Networks, and Matching Networks for image classification in Indian Sign Language. We evaluate these methods based on their classification accuracy. Our findings provide valuable insights into the effectiveness of different few-shot learning techniques.