This research focuses on enhancing machine comprehension in multilingual Natural Language Processing (NLP) environments. Despite advancements in pre-trained models, creating custom models still requires more resources. To overcome this, the study explores FewShot Learning (FSL), a Meta-Learning approach inspired by human learning efficiency, asserting that machines can adeptly learn from minimal examples and task descriptions. The methodology unfolds in two dimensions: practical application and theoretical exploration. In practical terms, Python constructs models using pre-trained frameworks—BERT, DistilBERT, ELECTRA, and MiniLM. These Few-Shot models undergo meticulous evaluation for efficiency and accuracy, especially in processing unseen data with minimal support for contextual understanding. Simultaneously, the theoretical facet involves a comprehensive literature review, shedding light on FSL's effectiveness in diverse NLP contexts. Preliminary findings suggest FSL's promising role in addressing multilingual challenges in NLP, acknowledging limitations in complex linguistic scenarios. This study offers valuable insights into FSL's practical applications and limitations, laying the foundation for future investigations. The nuanced exploration contributes to a balanced understanding of FSL's applicability, potentially guiding the development of more advanced and resource-efficient NLP models.