Pakistan has an annual deforestation rate of 4.6% which is the second highest in Asia. It has been described by the Food and Agriculture Organization (FAO) that the deforestation rate increased from 1.8–2.2% within two decades (1980–2000 and 2000–2010). KPK (Khyber Pakhtunkhwa), Pakistan’s northwestern province, holds 31% of the country’s total forest resources, the majority of which are natural forests. The Malam Jabba region, known for its agro-forestry practices, has undergone significant changes in its agricultural, forestry, and urban development. Agricultural and built-up land increased by 77.6% in the last four decades, and significant changes in land cover especially loss in forest, woodland, and agricultural land were observed due to flood disasters since 1980. For assessing and interpreting land-cover dynamics, particularly for changes in natural resources such as evergreen forest cover, remote sensing images are valuable assets. This study proposes a framework to assess the changes in vegetation cover in the Malam Jabba region during the past four decades with Landsat time series data. The random forest classifier (RF) was used to analyze the forest, woodland, and other land cover changes over the past four decades. Landsat MMS, TM, ETM+, and OLI satellite images were used as inputs for the random forest (RF) classifier. The vegetation cover change for each period was calculated from the pixels using vegetation indices such as NDVI, SAVI, and VCI. The results show that Malam Jabba’s total forest land area in 1980 was about 236 km2 and shrank to 152 km2 by 2020. The overall loss rate of evergreen forests was 35.3 percent. The mean forest cover loss rate occurred at 2.1 km2/year from 1980 to 2020. The area of woodland forest decreased by 87 km2 (25.43 percent) between 1980 and 2020. Other landcover increased by 121% and covered a total area of 178 km2. The overall accuracy was about 94% and the value of the kappa coefficient was 0.92 for the change in forest and woodland cover. In conclusion, this study can be beneficial to researchers and decision makers who are enthusiastic about using remote sensing for monitoring and planning the development of LULC at the regional and global scales. © 2023 by the authors.