Integrated Assessment Models (IAMs) are essential tools for projecting future environmental variables under diverse environmental, economic, and technological scenarios. However, their computational intensity limits accessibility and application scope. We present ML-IAM v1.0, the first machine learning emulator trained on the IPCC AR6 Scenarios Database to replicate IAM functionality across diverse model families. Our best-performing model, XGBoost, achieves an <em>R²</em> of 0.97 against original IAM data, outperforming the more complex models Long Short-Term Memory (LSTM) and Temporal Fusion Transformer (TFT). ML-IAM v1.0 generates results for 2,000 scenarios in 50 seconds and can produce predictions for any IAM family. This enables rapid exploration of climate scenarios, complementing traditional IAMs with efficient, scalable computation.