Climate change requires urgent action to reduce greenhouse gas emissions. In response to the urgent need for accurate carbon emission forecasting to support global and national carbon neutrality goals, this paper presents a predictive framework for carbon emissions in City X, utilizing the Long Short-Term Memory (LSTM) network model. The study integrates the Kaya model and the Logarithmic Mean Divisia Index (LMDI) for precise carbon accounting and identifies the key factors influencing emissions. Additionally, it employs logistic regression, ARIMA, and the least squares method to forecast population, GDP, and energy consumption, respectively. The LSTM model is innovatively applied to predict regional carbon emissions and offer policy recommendations for achieving carbon neutrality. The study presents three distinct scenarios for dual carbon targets, offering valuable insights for governments’ green policy development and advancing both theoretical and practical approaches to sustainable urban planning.