Abstract: Remote Sensing (RS) technologies have emerged as indispensable tools for mapping agricultural landscapes, offering unprecedented capabilities for monitoring crop health, assessing land use changes, and optimizing agricultural practices. This study provides an exhaustive review of the utilization of RS techniques in agricultural mapping. Beginning with an introduction to the fundamental principles of RS, the study explores the various platforms and sensors commonly employed in agricultural mapping activities. It explores the complexity of data acquisition, emphasizing preprocessing techniques essential for enhancing data quality and accuracy. The study then discusses image classification methodologies using advanced artificial intelligence (AI)-driven techniques such as machine learning (ML), deep learning (DL), and transformers-based algorithms for crop type classification and land cover mapping. It highlights the significance of feature extraction and selection in refining agricultural mapping outputs. Furthermore, the study showcases a range of applications of RS in agriculture, from monitoring crop health and detecting anomalies to estimating biophysical parameters critical for yield prediction and resource management. Real-world case studies exemplify the practical implementation of RS technologies in diverse agricultural settings. Despite its immense potential, RS in agricultural mapping presents certain challenges, including data availability, spatial and temporal resolution limitations, and the need for robust validation frameworks. The study addresses these challenges and offers insights into emerging trends and advancements poised to shape the future of agricultural mapping.