Machine learning frameworks for climate projection and adaptation planning in Senegal remain underexamined despite the country's acute vulnerability to Sahelian climate variability. This scoping review synthesises the state of the art in computational models applied to Senegalese climate prediction and adaptation planning, focusing on model architecture, data sources, and validation practices. The review follows the PRISMA-ScR framework, analysing 73 peer-reviewed studies published between 2015 and 2025, sourced from IEEE Xplore, Scopus, and Web of Science. Key findings reveal that 68% of studies employ ensemble learning methods—predominantly random forests and gradient boosting—for rainfall and temperature forecasting, yet only 12% incorporate uncertainty quantification via Bayesian inference or conformal prediction. A typical model is expressed as $\hat{y}_t = \sum_{i=1}^{n} w_i f_i(\mathbf{x}_t) + \epsilon_t$, where weights $w_i$ are optimised on historical ERA5 reanalysis data (1981–2020). The review identifies a critical gap: no existing framework integrates downscaled CMIP6 projections with local socio-economic adaptation indicators, and reported confidence intervals for prediction errors exceed ±2.5°C for seasonal forecasts. This paper contributes the first systematic mapping of ML frameworks for climate adaptation in Senegal, introducing a taxonomy that categorises models by predictive horizon, input resolution, and adaptation domain. A concrete result is that only 8% of studies validate models against ground-station data from the Agence Nationale de l'Aviation Civile et de la Météorologie. The findings imply that future frameworks must embed uncertainty-aware architectures and region-specific validation protocols to support actionable adaptation planning in data-sparse West African contexts.