This study evaluates genomic prediction methods for single cross maize hybrids not tested in multi-environment trials (MET), focusing on grain yield (GY) and female flowering time (FFT). The research compares statistical methods like Genomic Best Linear Unbiased Prediction (GBLUP) with non-additive effects and machine learning methods such as bagging (Bag), random forest (RF), and boosting (Boost). The results show that both approaches are effective for predicting hybrid performance in specific environments. GBLUP is more efficient when accurate variance component modeling is performed, while machine learning methods can capture non-additive effects without prior assumptions. Predicting hybrids not evaluated in any field trials is more challenging than predicting hybrids in sparse designs. The study highlights the potential of both methodologies in maize breeding programs. The best method depends on the specific case, with GBLUP-AD showing superior performance for traits influenced by dominance effects. Machine learning methods, while competitive, may not perform as well in small datasets. The study also demonstrates the cost-effectiveness of genomic prediction in reducing phenotyping costs and increasing the number of hybrids evaluated. Overall, both statistical and machine learning approaches are valuable tools for improving maize breeding efficiency.This study evaluates genomic prediction methods for single cross maize hybrids not tested in multi-environment trials (MET), focusing on grain yield (GY) and female flowering time (FFT). The research compares statistical methods like Genomic Best Linear Unbiased Prediction (GBLUP) with non-additive effects and machine learning methods such as bagging (Bag), random forest (RF), and boosting (Boost). The results show that both approaches are effective for predicting hybrid performance in specific environments. GBLUP is more efficient when accurate variance component modeling is performed, while machine learning methods can capture non-additive effects without prior assumptions. Predicting hybrids not evaluated in any field trials is more challenging than predicting hybrids in sparse designs. The study highlights the potential of both methodologies in maize breeding programs. The best method depends on the specific case, with GBLUP-AD showing superior performance for traits influenced by dominance effects. Machine learning methods, while competitive, may not perform as well in small datasets. The study also demonstrates the cost-effectiveness of genomic prediction in reducing phenotyping costs and increasing the number of hybrids evaluated. Overall, both statistical and machine learning approaches are valuable tools for improving maize breeding efficiency.