Teacher productivity is a critical determinant of educational quality and student success in public secondary schools, yet Baringo North and Kesses sub-counties face notable challenges in this area, as evidenced by multiple reports of teacher underperformance in various sub-counties. This situation not only impairs the learning experiences of students but also undermines broader educational goals and national development initiatives. As the backbone of educational systems, teachers shape knowledge acquisition and significantly influence student outcomes. Higher teacher productivity is widely recognized to correlate with improved student learning achievements. However, numerous reports highlight instances of teacher underperformance in Baringo north and Kesses sub-counties. The study objective was to investigate the selected factors influencing teacher productivity. The theory which guided the study was the job demand resource theory framework. The study used the concurrent mixed method research design (QUANT + qual). The accessible population of the study was teachers, heads of departments, and principals of the selected schools in Kesses and Baringo North sub-counties. The researcher used Nassiuma's (2000) formula to get the sample size. A sample of 125 participants was selected using simple random sampling, and five key informants were selected purposively. The researcher's supervisors and experts from the Department of Educational Foundations assisted in validating the research instruments (questionnaire and interview schedule) by checking the research instrument against the study objectives. Questionnaire piloting was done in Moiben sub-county to test the reliability. A coefficient of .787 was obtained, indicating the reliability of the instruments. Questionnaires were administered to teachers and heads of departments, and key informant interviews (KII) were conducted among principals. Quantitative data was analysed using Statistical Package for Social Science (SPSS) software. Descriptive statistics (percentages and frequencies) and inferential statistics (simple linear regression) were used, while qualitative data was analysed using QDA Miner software. A simple regression analysis was performed to determine the influence of selected parameters on teacher productivity. The results obtained were Y=4.199-.0.049. From the regression model, for every unit change in selected factors, teacher productivity changes by -.0449. This shows that increased selected factors lead to decreased teacher productivity. The results were Y=1.590+.666. From the regression model, for every unit change in the factors affecting teacher productivity teacher productivity changes by .666s. The study recommends that the Ministry of Education should work to mitigate the factors affecting teacher productivity in both areas. This will significantly contribute to improving the productivity of teachers.