Speech analysis offers a non-invasive method for assessing emotional and cognitive states through acoustic correlates, including spectral, prosodic, and voice quality features. Despite growing interest, research remains inconsistent in identifying reliable acoustic markers, providing limited guidance for researchers and practitioners in the field. This review identifies key acoustic correlates for detecting negative emotions, stress, and cognitive load in speech. A systematic search was conducted across four electronic databases: PubMed, PsycInfo, Web of Science, and Scopus. Peer-reviewed articles reporting studies conducted with healthy adult participants were included. Thirty-eight articles were reviewed, encompassing 39 studies, as one article reported on two studies. Among all features, prosodic features were the most investigated and showed the greatest accuracy in detecting negative emotions, stress, and cognitive load. Specifically, anger was associated with elevated fundamental frequency (F0), increased speech volume, and faster speech rate. Stress was associated with increased F0 and intensity, and reduced speech duration. Cognitive load was linked to increased F0 and intensity, although the results for F0 were overall less clear than those for negative emotions and stress. No consistent acoustic patterns were identified for fear or anxiety. The findings support speech analysis as a useful tool for researchers and practitioners aiming to assess negative emotions, stress, and cognitive load in experimental and field studies.