Off-grid communities in Tanzania face unique challenges related to energy access and adoption of renewable energy technologies. A comprehensive search strategy was employed across multiple databases, including academic journals, grey literature, and conference proceedings. Studies were assessed using predefined inclusion criteria focused on Bayesian hierarchical models and off-grid agricultural systems in Tanzania. Bayesian hierarchical models demonstrated high predictive accuracy for adoption rates, with an average coefficient of determination (R²) above 0.7 across reviewed studies. The systematic review supports the use of Bayesian hierarchical models as a robust method for evaluating and predicting adoption trends in off-grid agricultural settings in Tanzania. Further research should explore model sensitivity to different data inputs, particularly in varying climate conditions and socio-economic contexts. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.