Harmful algal blooms (HAB; Karenia brevis) occurrences have been reported from the coastal waters of Charlotte County in southwest Florida. We developed multivariate regression models that relate reported (January 2010 to October 2017) bloom occurrences to observations extracted from archival remote sensing data (Moderate Resolution Imaging Spectroradiometer [MODIS]) to accomplish the following: (1) identify factors controlling HAB propagation, (2) predict algal bloom distribution (same day, and 1, 2, and 3 days in advance), and (3) develop fully automated system for data distribution via a web-based GIS platform. These tasks were accomplished through three main steps: (1) automatic downloading and processing of daily MODIS products using SeaDAS software to extract relevant remote sensing variables (euphotic depth, wind direction, ocean chlorophyll three-band algorithm for MODIS [Chlorophyll a OC3M], wind speed, chlorophyll a Generalized Inherent Optical Property [GIOP], Fluorescence Line Height [Flh], diffused attenuation coefficient for downwelling irradiance at 490 nanometer [Kd_490], chlorophyll a Garver-Siegel- Maritorena [GSM], Turbidity index, Particulate backscattering coefficient at 547 nm [bbp_547_giop] and sea surface temperature [SST]), (2) development and calibration of multivariate regression models using relevant remote sensing and static variable (distance from river mouth, bathymetry) inputs for same day mapping and forecasting of HAB occurrences, and (3) automated posting of model outputs on a web-based GIS (http://mgs.geology.wmich.edu/bloom/). Findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature [SST], chlorophyll a [OC3M] and distance from the river mouth, and (2) the model predictions were successful at 90% for same day mapping and 65%, 72% and 71% for the one, two and three days in advance predictions, respectively.