BACKGROUND: The acquisition of isoniazid (INH) resistance is alarming, considering its importance as a key drug that forms the core of multidrug treatment regimens for tuberculosis (TB). Genetic mutations in the katG and inhA promoter regions play crucial roles in INH resistance, but their prevalence varies geographically. This study aimed to identify the most common mutations in the katG and inhA genes in INH-resistant (INH-R) Mycobacterium tuberculosis (MTB) clinical isolates in Cameroon. The research also explored the relationships between these mutations and patients' demographics (age, sex, and sample type). METHODS: We conducted a retrospective cross-sectional laboratory-based study on 500 INH-R isolates (with or without resistance to other first-line drugs) at the National Tuberculosis Reference Laboratory (NTRL) in Cameroon between January 2014 and December 2020. GenoType MTBDRplus assay was performed on the retrieved isolates and the frequency of katG and inhA mutations were calculated. Chi-square tests were utilized to assess the associations between these mutations and patients' age, sex and sample type. RESULTS: A total of 410 (85.8%) culture-positive MTB isolates were analyzed, with a male-to-female ratio of 228 (55.6%) to 182 (44.4%) and an average age of 36.3 ± 13.4 years. Mutations in the katG and inhA genes were detected in 354 (86.3%) of cases, while 56 (13.7%) showed no mutations. Among the INH-R isolates, mutations in katG, inhA, and dual katG and inhA genes were present in 247 (60.2%), 76 (18.5%), and 31 (7.6%) isolates, respectively. Our analysis revealed significant associations between mutation prevalence and patient characteristics. CONCLUSION: This study reaffirmed the importance of the katG S315T substitution as a key indicator of INH resistance, with the inhA C-15T mutation providing additional support. However, a notable proportion of isoniazid-resistant isolates did not exhibit these mutations, underscoring the need to comprehend resistance mechanisms. Given that these mechanisms are strongly associated with varying levels of INH resistance, it is crucial that TB management strategies incorporate genetic profiling alongside patient demographics to optimize treatment outcomes and enhance control measures.