Raw data collected from diverse sources often contains noise, inconsistencies, and missing values, which can negatively impact the performance of AI models. Data cleaning and preprocessing are critical steps to ensure data quality and reliability. AI automates these processes using techniques such as anomaly detection, outlier removal, and imputation of missing values. Machine learning algorithms can identify patterns in the data to predict and correct errors, reducing the need for manual data cleansing. Natural Language Processing (NLP) is used to preprocess text data by performing tokenization, stemming, and removing stop words. Image and video data undergo preprocessing techniques like normalization, resizing, and augmentation to enhance model performance. This automation accelerates data preparation, allowing data scientists to focus on model development and analysis.