This study investigates the effectiveness of cross lingual synopsis generation across nine languages-Indonesian, Dutch, English, Vietnamese, Russian, Korean, Portuguese, Hindi, and French. Utilizing advanced NLP techniques, we develop synopsis generation models capable of extracting key information from diverse textual sources. Unlike previous works, we focus on unique challenges and optimizations specific cross lingual contexts. Our methodology incorporates clustering-based approaches with language embedding, which we evaluate comprehensively to highlight performance variations across languages. Additionally, we conduct an error analysis to identify language-specific challenges. Our findings provide valuable insights into cross-lingual transferability and pave the way for more accessible synopsis generation technologies that cater to diverse linguistic communities, thereby advancing the field of cross lingual synopsis generation.