Machine Learning–Based Prediction of Atmospheric CO₂ Concentration: A Year– Month Trend analysis

Atmospheric carbon dioxide (CO₂) remains the principal driver of contemporary climate change, yet accurately forecasting its temporal evolution requires models capable of capturing complex nonlinear and seasonal dynamics. In this study, we conduct a comprehensive evaluation of thirteen supervised machine-learning algorithms to model and predict long-term atmospheric CO₂ concentrations using multi-decadal monthly and annual observational datasets. The dataset encompasses several decades of global CO₂ measurements, enabling a detailed investigation of both persistent climatological trends and short-term oscillatory variations. All models were trained under a unified workflow and assessed using a standardized performance matrix comprising R², RMSE, and MAE. Among the tested algorithms, Random Forest Regression and a multilayer Artificial Neural Network (ANN) consistently outperformed other classical and ensemble methods, achieving R² values greater than 0.95 and demonstrating exceptional robustness against noise and seasonal irregularities. Time-series diagnostics further reveal a sustained, near-exponential increase in global CO₂ levels, reflecting intensified anthropogenic influence, reduced carbon-sink efficiency, and accelerating feedback mechanisms in the Earth system. The results highlight the utility of machine-learning techniques as reliable and scalable tools for atmospheric CO₂ forecasting, offering improved sensitivity to nonlinearities compared to traditional statistical approaches. Importantly, the analytical framework developed in this work is extensible and can readily integrate additional environmental variables such as oceanic carbon-flux parameters, biospheric exchange indices, remote-sensing products, or even emerging acoustic-signature datasets to construct more holistic, multimodal prediction systems. By establishing a strong methodological baseline, this study contributes to the advancement of next-generation climate-monitoring and predictive-analytics systems, supporting ev