Data quality is a critical factor that directly impacts the performance and reliability of AI models. In Big Data environments, data often comes from diverse sources, including sensors, social media, transactional systems, and IoT devices. This leads to challenges such as missing values, inconsistencies, duplicate records, and noisy data. AI addresses these issues through automated data cleaning and preprocessing techniques. Machine learning algorithms can detect anomalies, correct errors, and impute missing values by learning from historical data patterns. Additionally, AI models can assess data accuracy, completeness, and consistency in real-time, ensuring that the insights derived from Big Data are reliable and actionable. Poor data quality can lead to biased predictions, inaccurate forecasts, and flawed business decisions, highlighting the need for robust data governance and continuous monitoring in AI-driven systems.