This study examines the complex interactions between CO2 emissions, economic growth, and energy consumption across various classifications of countries. In this study, we propose SSM-FastICANet, a novel hybrid model that integrates state space models with independent component analysis and a diagonal structure for efficient and accurate economic growth forecasting a predictive model that can forecast economic growth by analyzing energy consumption patterns and emission levels, while also pinpointing the distinct impacts of CO2 emissions and energy usage. Employing a time-series dataset and an innovative hybrid methodology that combines state space models (SSMs) with fast independent component analysis (FastICA), the study reveals unique interaction patterns among these variables. The FastICA method aids in uncovering essential underlying patterns and reducing dimensionality, whereas the SSM architecture proficiently captures temporal dependencies and emphasizes the most pertinent input features for precise prediction and impact detection. The model utilizes entropy, kurtosis, and variance to filter independent components, guaranteeing that the chosen features are statistically significant, locally structured, and resilient to noise. The findings demonstrate that SSM-FastICANet significantly enhances feature selection, model adaptability, and interpretability, yielding reliable predictions over various time intervals. It enhances the comprehension of the energy-economy-environment relationship and offers a solid framework for policymakers to develop strategies that foster sustainable economic growth while reducing environmental impact.