Aim: To identify periodontopathogenic bacteria in periodontal pockets and subgingival plaque of patients with periodontitis in Karbala, Iraq, using 16S rRNA gene sequencing, the gold standard for molecular identification of microorganisms. Materials and Methods: Molecular techniques based on 16S rRNA gene analysis provide reliable identification of bacterial species associated with periodontal infections. A crosssectional study was conducted with 26 patients, ranging in age from 20 to 50 years. The patients were excluded of the study if they had been on systemic antibiotics or periodontal therapy within the past three months. Every sample was aseptically collected with informed consent. The sterile paper points were used to take sample of the subgingival plaque, and they were inserted in the deepest periodontal pockets with a duration of 20 30 seconds. The samples were instantly frozen in sterile microtubes with transport buffer and frozen at -20 o C until further molecular analysis. Results: The study population consisted of 65.38% females and 34.62% males. The most frequent clinical findings were gingival bleeding (55%) and inflammation (33%), with no significant differences observed between sexes. Molecular analysis revealed a high prevalence of Porphyromonas gingivalis (20/26) and Tannerella forsythia (18/26). Veillonella parvula was detected in 12 patients, while Treponema denticola was identified in 11 patients. Actinobacillus actinomycetemcomitans and Fusobacterium nucleatum were detected at lower frequencies, in 3 and 2 patients, respectively. Successful amplification of the 16S rRNA gene was confirmed by the presence of a ~1500 bp band in all samples. Conclusions: Sequencing PCR is a powerful tool for the diagnosis of the subgingival microbiota in periodontitis, showing that Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola are the most frequently encountered pathogens in the sampled population. Moreover, the strong link between Treponema denti cola and diabetes mellitus highlights the need to take into account systemic health factors when analysing microbiological data.