Salmonellosis is the second-most commonly reported foodborne gastrointestinal infection in the European Union and a major contributor to foodborne outbreaks globally. Salmonella serotyping differentiates typhoidal strains requiring antibiotic therapy (e.g., serovars Typhi, Paratyphi A, Paratyphi B-d-tartrate negative, Paratyphi C) from typically self-limiting non-typhoidal Salmonella (NTS) strains, making precise identification essential for appropriate treatment and epidemiological tracking. At the same time, the ability to identify the serogroup of Salmonella, regardless of which of the above two groups it belongs to, provides an important initial epidemiological indication that is useful for case management by competent health authorities. This study evaluates the effectiveness of ATR-FTIR spectroscopy coupled with a machine learning algorithm to identify four key Salmonella enterica serogroups (B, C1, D1—including typhoidal strains such as S. Typhi—and E1) directly from solid monomicrobial cultures without sample pretreatment. The system was paired with I-dOne software v2.2 already able to detect Salmonella spp., possibly leading to the characterisation of both the species and serotype from one colony. The multivariate classification model was trained and validated with 248 strains, with an overall accuracy of >98% over 113 samples. This approach offers a potential rapid alternative for clinical labs without serotyping facilities.