The rapid growth of the digital era has resulted in the generation of huge and complex datasets from sectors such as healthcare, agriculture, environmental monitoring, finance, and social media. This has led to the emergence of Big Data, commonly characterized by the five V?s: volume, velocity, variety, veracity, and value. However, data alone cannot provide meaningful knowledge without proper analysis. Statistics plays a crucial role in Big Data by offering systematic tools for organizing, modeling, and interpreting large-scale datasets. Statistical techniques such as regression analysis, hypothesis testing, clustering, factor analysis, and predictive modeling help in identifying patterns, relationships, and trends, thereby supporting evidence-based decision-making. Modern tools including R, Python, SPSS, SAS, Apache Spark, Tableau, and Power BI enhance the processing and visualization of Big Data. Despite its benefits, challenges such as computational complexity, storage issues, privacy risks, and ethical concerns remain significant. Future developments must focus on scalable and transparent analytical methods for sustainable Big Data applications.