Urban energy consumption plays a critical role in global energy use, with cities accounting for over 70% of energy demands and greenhouse gas emissions, making it essential to implement innovative, energy-efficient solutions, predictive management, and sustainable building practices to reduce these impacts. The Urban Heat Island (UHI) effect, worsened by overpopulation and rapid urbanization, further aggravates energy demands and urban warming. Addressing these challenges requires the integration of renewable energy technologies and advanced analytics, including Machine Learning (ML), to optimize urban energy systems and predict high-demand periods. This paper assesses the impact of urban energy consumption on efficiency challenges and the role of renewable energy, based on articles from leading journals over the past two decades. It evaluates the effectiveness of renewable energy solutions, such as solar, wind, geothermal, and biomass, in mitigating urban warming and advancing sustainable energy practices, with a focus on ML-driven predictive strategies. The analysis begins with a review of energy consumption in urban buildings, highlighting the UHI effect and primary drivers of energy use and efficiency challenges. It then examines urban warming and the implementation of renewable energy solutions optimized for specific locations using data-driven techniques. Finally, the paper discusses the challenges and future prospects of renewable energy transitions in urban environments, emphasizing that adopting renewable technologies, combined with ML-enabled energy management, can reduce carbon footprints by up to 40%, support the 2030 targets of lowering per capita energy use and CO2 emissions by 30%, and advance the global goal of climate neutrality by 2050.