In the digital era, intelligent transportation systems enhance urban mobility through plug-in hybrid electric vehicle (PHEV) integration. This study identifies opportunities to improve PHEV utility by enabling productive activity during charging/discharging periods instead of idle waiting time. We address smart city PHEV service challenges through a machine learning-enabled fog computing platform featuring an Intelligent Decision Making System (IDMS). This system helps mobilityaware PHEV users optimize multiple services with minimal costs and decision-making delays. The IDMS offers precise predictions for accessing various services at single destinations. Performance evaluations confirm our decision tree-based algorithm delivers superior accuracy compared to existing solutions, significantly enhancing PHEV user experience in smart city environments. The approach successfully balances user convenience and cost efficiency.