Biometric technologies are rapidly becoming central to identity verification, national security, and personalized services. However, as the reliance on biometric systems grows, so do the concerns around dataset bias, a silent but significant threat to fairness, accuracy, and inclusivity. This paper investigates the presence and impact of dataset bias in biometric research, shedding light on how skewed data representation can lead to discriminatory outcomes, particularly for underrepresented groups. We explore the roots of bias, ranging from limited demographic diversity in training datasets to socio-technical factors influencing data collection practices. Through real-world case studies and critical analysis, this study urges researchers and developers to adopt more ethical, transparent, and inclusive data strategies. The goal is not just to improve biometric system performance but to ensure that these technologies serve all individuals equally, regardless of race, gender, or age. Tackling dataset bias isn't just a technical issue; it's a matter of social justice and trust in an increasingly digital world.