This review discusses the extraction of surface water bodies using optical remote sensing images. Surface water mapping (SWM) is crucial for studying hydrological and ecological phenomena, water resource management, environmental conservation, and disaster preparedness. Recent urbanization, overuse, and environmental degradation have significantly impacted surface water bodies. Advances in remote sensing data and technologies have led to a new era for SWM. Timely and accurate SWM is essential for water resource preservation and planning. This paper reviews the extraction of surface water bodies from optical sensors using spectral indices (SI), machine learning (ML), deep learning (DL), and spectral unmixing, with a comprehensive overview of satellite data, study areas, methodologies, results, advantages, and disadvantages over the last decade. The review shows that DL outperforms ML and SI due to its incorporation of key elements in network design, such as skip connections, dilation convolution, attention mechanisms, and residual blocks. Spectral unmixing addresses the mixed pixel misclassification problem. Some SI, ML, and DL methods are implemented, and the results are discussed. Integrating DL with spectral unmixing, fusing multisource data (SAR and optical), and integrating with ancillary data (DEM) is the future direction for improved SWM. Water is a precious and finite resource essential for maintaining ecosystems and biodiversity, supporting economics, and promoting global human development. Surface water bodies, including lakes, ponds, rivers, reservoirs, wetlands, seas, and oceans, store water on Earth's surface. Natural and human-induced factors influence surface water bodies, affecting their dynamics. Traditional methods for measuring water extent are labor-intensive and limited in spatial and temporal coverage. Remote sensing (RS) technologies offer a transformative solution by visually representing the Earth's surface. Advances in satellite sensors, including optical and microwave, enable detailed information about the Earth's surface. The comprehensive coverage and frequent observation capabilities of satellite images make them invaluable for various applications. RS images are captured by microwave and optical sensors, each with its own advantages and limitations. Combining DEM with satellite images provides accurate water body extraction. With the massive amount of RS data, advanced processing technology is required to enhance the efficiency and accuracy of water body extraction. The various techniques used for SWM from optical sensors are the primary focus of this article.This review discusses the extraction of surface water bodies using optical remote sensing images. Surface water mapping (SWM) is crucial for studying hydrological and ecological phenomena, water resource management, environmental conservation, and disaster preparedness. Recent urbanization, overuse, and environmental degradation have significantly impacted surface water bodies. Advances in remote sensing data and technologies have led to a new era for SWM. Timely and accurate SWM is essential for water resource preservation and planning. This paper reviews the extraction of surface water bodies from optical sensors using spectral indices (SI), machine learning (ML), deep learning (DL), and spectral unmixing, with a comprehensive overview of satellite data, study areas, methodologies, results, advantages, and disadvantages over the last decade. The review shows that DL outperforms ML and SI due to its incorporation of key elements in network design, such as skip connections, dilation convolution, attention mechanisms, and residual blocks. Spectral unmixing addresses the mixed pixel misclassification problem. Some SI, ML, and DL methods are implemented, and the results are discussed. Integrating DL with spectral unmixing, fusing multisource data (SAR and optical), and integrating with ancillary data (DEM) is the future direction for improved SWM. Water is a precious and finite resource essential for maintaining ecosystems and biodiversity, supporting economics, and promoting global human development. Surface water bodies, including lakes, ponds, rivers, reservoirs, wetlands, seas, and oceans, store water on Earth's surface. Natural and human-induced factors influence surface water bodies, affecting their dynamics. Traditional methods for measuring water extent are labor-intensive and limited in spatial and temporal coverage. Remote sensing (RS) technologies offer a transformative solution by visually representing the Earth's surface. Advances in satellite sensors, including optical and microwave, enable detailed information about the Earth's surface. The comprehensive coverage and frequent observation capabilities of satellite images make them invaluable for various applications. RS images are captured by microwave and optical sensors, each with its own advantages and limitations. Combining DEM with satellite images provides accurate water body extraction. With the massive amount of RS data, advanced processing technology is required to enhance the efficiency and accuracy of water body extraction. The various techniques used for SWM from optical sensors are the primary focus of this article.