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Since Large Language Models are increasingly being integrated into AI systems, keeping consistent fair reasoning performance over numerous languages is very essential. Kannada models face significant limitations, whereas English LLMs perform well on a variety of reasoning tasks, including relational, abstract, deductive, and logical reasoning. The absence of massive annotated datasets, the agglutinative character and difficult morphology of the language, and the variations between the several spoken forms and the developed standard written Kannada are the main causes of these difficulties. This study demonstrates an important distinction in performance by reviewing and analyzing recent research as well as benchmarks like MILU, which reports accuracy of up to 85% on specific logical reasoning tasks. It also identifies a number of possibilities to close this gap in reasoning. Discussed were some of the effective approaches, such as multilingual transfer learning with models like mT5 and IndicBERT advanced prompting with Chain-of- Thought and synthetic data creation using back-translation and template-based sentence construction. These would likely increase the performance of Kannada models on the reasoning benchmarks by as much as 15% to 20%., comparative studies have noted. With a focus on linguistically and culturally grounded benchmarks, this work provides an organized framework to systematically improve reasoning in Indic LLMs. It encourages future research into multimodal reasoning and scalable cross-lingual techniques in order to ensure greater inclusivity in building equally accessible AI systems for diverse language backgrounds.