Labeling data is an essential step in training supervised machine learning models. It enables various applications, from image classification in computer vision to sentiment analysis in natural language processing. However, manually labeling data is a time-consuming process and can be subject to bias. On the other hand, automated methods may compromise the quality of the data. Additionally, there are several challenges in data labeling that include scalability, bias, drift, and privacy concerns. It is crucial to balance time, cost, and quality when dealing with data. Looking into the future, we can expect larger and more complex datasets, with automation through predictive annotation and transfer learning becoming more prevalent. Furthermore, there will be an even greater emphasis on data quality for critical applications. Therefore, efficient and high-quality data labeling is essential for the continued advancement of AI and ML.