  Global warming's impact on high temperatures in various parts of the world
has raised concerns. This study investigates long-term temperature trends in
four major Ghanaian cities representing distinct climatic zones. Using NASA's
Prediction of Worldwide Energy Resource (POWER) data, statistical analyses
assess local climate warming and its implications. Linear regression trend
analysis and eXtreme Gradient Boosting (XGBoost) machine learning predict
temperature variations. Land Surface Temperature (LST) profile maps generated
from the RSLab platform enhance accuracy. Results reveal local warming trends,
particularly in industrialized Accra. Demographic factors aren't significant.
XGBoost model's low Root Mean Square Error (RMSE) scores demonstrate
effectiveness in capturing temperature patterns. Wa unexpectedly has the
highest mean temperature. Estimated mean temperatures for mid-2023 are: Accra
27.86{\deg}C, Kumasi 27.15{\deg}C, Kete-Krachi 29.39{\deg}C, and Wa
30.76{\deg}C. These findings improve understanding of local climate warming for
policymakers and communities, aiding climate change strategies.
