The global proliferation of Generative Artificial Intelligence (GenAI) has led to the increasing presence of AI-generated text across a wide spectrum of topics, ranging from everyday content to critical and specialized domains. Often, individuals are unaware that the text they interact with was produced by AI systems rather than human authors, leading to instances where AI-generated content is unintentionally combined with human-written material. In response to this growing concern, we propose a novel approach as part of the Multi-Domain AI-Generated Text Detection (M-DAIGT) shared task, which aims to accurately identify AI-generated content across multiple domains, particularly in news reporting and academic writing. Given the rapid evolution of large language models (LLMs), distinguishing between human-authored and AI-generated text has become increasingly challenging. To address this, our method employs fine-tuning strategies using transformer-based language models for binary text classification. We focus on two specific domains, news and scholarly writing, and demonstrate that our approach, based on the DeBERTa transformer model, achieves superior performance in identifying AI-generated text. Our team, CNLP-NITS-PP, achieved 5th position in Subtask 1 and 3rd position in Subtask 2.