Bias in news reporting significantly influences public perception, particularly in sensitive and polarized contexts like the Israel-Gaza conflict. Detecting bias in such cases presents unique challenges due to political, cultural, and ideological complexities, often amplifying disparities in reporting. While prior research has addressed media bias and dataset fairness, these approaches inadequately capture the nuanced dynamics of the Israel-Gaza conflict. To address this gap, we propose an NLP-based framework that leverages Nakba narratives as linguistic resources for bias detection in news coverage. Using a multilingual corpus focusing on Arabic texts, we apply rigorous data cleaning, pre-processing, and methods to mitigate imbalanced class distributions that could skew classification outcomes. Our study explores various approaches, including Machine Learning (ML), Deep Learning (DL), Transformer-based architectures, and generative models. The findings demonstrate promising advancements in automating bias detection, and enhancing fairness and accuracy in politically sensitive reporting.