Clustering algorithms like K-Means are widely used in unsupervised learning due to their simplicity and interpretability. However, traditional clustering methods are agnostic to fairness and can result in biased groupings when sensitive attributes such as race or gender are embedded in the data. Existing fairness-aware methods like RFKM and FairKM improve equity but require manual hyperparameter tuning and often struggle to generalize across diverse datasets.AutoRFKM is a unified and extensible framework that combines representative fairness, demographic parity, and automated hyperparameter optimization – addressing multiple fairness dimensions within an unsupervised clustering context. Empirical evaluations on benchmark and real-world datasets demonstrate that AutoRFKM significantly improves clustering compactness, structural cohesion, and fairness distribution compared to prior baselines, while eliminating the need for manual tuning. The model is designed for practical deployment in fairness-critical applications, such as hiring pipelines, loan approval systems, and healthcare triage, where equitable unsupervised decision-making is essential. The proposed AutoRFKM has achieved much better performance compared to the state-of-the-art approaches in terms of average distance, variable distance, and many other metrics.