The deployment of Large Language Models(LLMs) has intensified concerns regarding thepropagation of societal stereotypes encodedwith web-scale training corpora. This pa-per presents a dual-paradigm framework spe-cially designed to address multilingual gender-inclusvity and counterfactual generation. Formultilingual gender-neutral text transformation,a fine-tuned mT5 encoder–decoder model per-forms controlled sentence rewriting with mini-mal edits while preserving semantic fidelity andgrammatical fluency. For counter-narrative gen-eration, the Llama-3 8B decoder-only model isemployed to generate empathetic and persua-sive responses through structured prompt-basedgeneration. The framework is evaluated usingdatasets from the LT-EDI ACL 2026 sharedtask across multiple languages, including En-glish, Tamil, Kannada, German, and Spanish.Experimental results demonstrate strong effec-tiveness in identifying and neutralizing gendermarkers, particularly in morphologically richlanguages, while the counter-narrative compo-nent achieves high performance in politeness,coherence, and relevance. Overall, the pro-posed approach contributes toward the develop-ment of responsible and inclusive multilingualNLP systems.