Fairness in machine learning remains challenging due to its ethical complexity, the lack of a universal definition, and the need for context-specific bias metrics. Existing methods also remain limited in handling intersectionality, multiclass settings, and broader flexibility and generality. To address these limitations, we introduce MIFair, a unified framework for bias assessment and mitigation based on mutual information. MIFair provides a flexible metric template and an in-processing mitigation method inspired by the Prejudice Remover, defining group fairness as statistical independence between prediction-derived variables and sensitive attributes, while establishing equivalences with widely used notions such as independence and separation. MIFair naturally supports intersectionality, complex subgroup structures, and multiclass classification and employs regularization-based training to reduce bias according to the selected metric. Its key advantage is its versatility: it consolidates diverse fairness requirements into a single coherent framework, enabling consistent benchmarking and facilitating practical adoption. Experiments on real-world tabular and image datasets show that MIFair effectively reduces bias, including in previously unaddressed multi-attribute scenarios, while maintaining strong predictive performance across all evaluated settings.