Currently, the High Resolution Rapid Refresh (HRRR) coupled with Smoke (HRRR-Smoke) and many other smoke forecasting models represent diurnal cycle of fire emissions by using a one-curve fits-all approach for modeling future FRP with a climatological curve. As is, it is quite challenging to accurately forecast fire emissions and smoke concentrations in rapidly changing fire behavior and weather conditions. Further, sometimes FRP values may not be available or are missing for an active fire. We propose improving upon older methods by introducing a machine learning model that integrates available Geostationary Operational Environmental Satellites (GOES) R satellite irradiance data and measured FRP with existing satellite-measured FRP values in addition to modeled meteorological values to better represent the FRP curve of individual fires based on current weather conditions and geospatial location. We compare difference machine learning (ML) methods, such as random forest and neural networks, to demonstrate a new way to fit a unique FRP curve to each pixel containing a fire. We evaluate the importance of different input sources going into the ML methods creating this curve and compare the ML models between themselves as well as with the performance of the current climate curve. This has impact on smoke modeling, biomass burning, and modeling potential carbon contributions into the atmosphere.