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Speed harmonization is an active traffic management strategy used for delaying the onset of traffic flow breakdown and mitigating congestion. It changes the speed limits throughout a roadway segment of interest based on prevailing traffic, weather, and road conditions. Implementations rely on fixed roadway sensors to collect traffic information and variable speed signs at fixed locations to display updated speeds. Moreover, most use a reactive rule-based decision tree to activate the control strategy. This setup faces three main challenges: 1) fixed infrastructure sensors do not provide a complete picture of traffic conditions and therefore can impact the accuracy of locating congestion, 2) communicating speed limit changes to drivers at fixed locations can result in an ineffective response depending on where the sign is located with respect to congestion, 3) reactive speed harmonization strategies are generally less effective than predictive ones. To overcome these limitations, this paper presents a predictive speed harmonization system that utilizes machine learning algorithms and V2I communications. The system collects detailed trajectories from connected vehicles to estimate current traffic properties, predict future traffic state, and broadcast new speed limits to connected vehicles accordingly. Simulation of multiple operational scenarios shows that the system can improve traffic flow stability, mitigate congestion, an reduce travel time. The system can also prevent traffic flow breakdown entirely in low traffic congestion conditions. Results also indicate that an optimal speed harmonization strategy requires control at the individual vehicle level where …