Since 2014, the Mexican Caribbean has faced an ecological and socio-economic crisis due to massive coastal landings of pelagic sargassum. This study presents a comprehensive methodology for sargassum detection using Machine Learning (ML) models applied to imagery from a coastal video monitoring station (EVMC). Three machine learning techniques were implemented to classify sargassum on sand and water, Support Vector Machines (SVM), Random Forest (RF), and a Multi-Layer Perceptron (MLP) Artificial Neural Network. The performance of these ground-based models was then compared against sargassum detections from Sentinel-2 satellite data using the Floating Algae Index (FAI) and the Normalized Difference Vegetation Index (NDVI). The results demonstrated high efficacy, with the MLP model proving most effective for detecting sargassum on sand with a F1-score > 0.86, whilst the Random Forest model performing best in water with a F1-score > 0.75. A significant positive correlation was found between the video-based detections and indices derived from satellite data, with NDVI showing a consistently stronger correlation in both environments. This study validates coastal video monitoring as a reliable tool for local sargassum quantification and suggests that fusing this high-resolution ground data with wide-area satellite imagery offers a promising path toward a more accurate and comprehensive sargassum monitoring system.