Significance Malaria elimination efforts are hindered by the prevalence of asymptomatic infections, which frequently go undetected and untreated. Consequently, there is a pressing need for improved diagnostic screening methods. Based on extensive collections of skin odors from human populations in Kenya, we report broad and consistent effects of malaria infection on human volatile emissions. Furthermore, we found that predictive models based on machine learning algorithms reliably determined infection status based on volatile biomarkers and, critically, identified asymptomatic infections with 100% sensitivity, even in the case of low-level infections not detectable by microscopy. These findings suggest that volatile biomarkers have significant potential for the development of robust, noninvasive screening methods for detecting symptomatic and asymptomatic malaria infections under field conditions.