Gender bias in multilingual language generation systems poses serious ethical and social issues, especially in languages with complex morphology. In this study, we propose a lightweight multilingual approach that employs instruction-guided fine-tuning of the mT5-small transformer model for gender-inclusive language generation. The framework accommodates five languages: English, German, Spanish, Tamil, and Kannada. The approach uses the task-prefix rewriting method to transform gender-specific sentences to their gender-neutral versions. The training data from different languages is combined into a single multi-lingual dataset for sequence-to-sequence fine-tuning. Beam search decoding with repetition constraints is used during inference to improve the quality of the output. The system’s performance is measured using GIFI, semantic similarity, and an overall combined score across all languages. Experimental results show that the system can eliminate gender-biased language while retaining semantic meaning in part across languages