Rice husk ash (RHA) is one of the main agricultural wastes that holds great potential for utilization in concrete production. Previous studies have mainly focused on its technical feasibility through experimental investigations, neglecting the crucial aspects of environmental and economic viability in practical applications. To address this gap and promote the efficient utilization of RHA in concrete, this study employs three advanced machine learning algorithms to develop predictive models for compressive strength. These models aim to design RHA Concrete (RHAC) with the minimum carbon footprint and financial cost. Among the algorithms used, the ant lion algorithm and eXtreme Gradient Boosting (ALO-XGB) model exhibit superior performance, demonstrating high accuracy across four indicators and showcasing their effectiveness in a Taylor diagram. The optimized model is further interpreted using the Shapley Additive Profile (SHAP). Additionally, multi-objective optimization is conducted using the ALO-XGB, to maximize the compressive strength while controlling material cost and CO₂ emissions. Our results provide the optimal mix ratios for RHAC within different strength ranges and offer insights into the influence of RHA content on mechanical, economic, and environmental factors. Specifically, the RHAC designed in this study can reach a compressive strength of 40–60 MPa while delivering significant environmental benefits. By replacing traditional concrete in housing and bridge structures, RHAC can effectively reduce the carbon footprint by 12.2–17.5 kg CO₂ eq./m³ in Italy. However, it is important to acknowledge that the current high cost of producing this sustainable concrete necessitates government incentives and subsidies to alleviate financial burdens on companies and facilitate market expansion.