Massive carbon dioxide (CO2) emissions drive climate change. Injecting CO2 into unconventional reservoirs achieves both enhanced oil recovery (EOR) and geological sequestration. However, simultaneously optimizing oil exchange ratio, CO2 storage, and net present value remains challenging. This study develops an integrated machine learning (ML)-based framework for multi-objective optimization of CO2-EOR. A high-resolution reservoir simulation was constructed from field data, and Latin hypercube sampling generated diverse scenarios for proxy training. Mantel's test quantified correlations between input parameters and performance metrics, showing that injection strategy strongly controls net present value, whereas geological properties dominate CO2 storage. Three ML models—random forest (RF), support vector regression, and artificial neural networks—were evaluated, with RF selected for its superior performance on small datasets. RF was embedded into an improved non-dominated sorting genetic algorithm II, enhanced with grey difference degree, crowding distance, and adaptive differential evolution to improve diversity and efficiency. Finally, the technique for order preference by similarity to ideal solution ranked Pareto-optimal solutions through integrating oil productivity, storage, and economics. The proposed framework operationalizes simultaneous high-efficiency tight oil recovery and field-scale CO2 geological storage, delivering quantitative design rules that embed low-carbon practice into upstream operations and advance the energy sector's greener and sustainable transition.