The presence of mistletoe in pine stands has expanded in recent decades, currently threatening Mediterranean forests. Mistletoe outbreaks can make the host trees more vulnerable to intense droughts, which are expected to increase due to climate change. We use multispectral (MS) and LiDAR UAV-derived data to classify Viscum album L. ssp. austriacum infestation levels at individual tree level in Scots pine (Pinus sylvestris L.) forests. First, spectral and structural differences between three infestation levels were assessed employing Kruskal–Wallis test with Benjamini–Hochberg correction and post hoc Dunn’s test for individual tree crowns. Second, classification algorithms were applied to evaluate infestation levels at the individual tree scale by combining UAV-derived datasets. The outcomes revealed significant differences between infestation levels in canopy cover and height based on LiDAR-derived metrics. Significant changes in vegetation vigor were also found through spectral and textural metrics. The highest classification accuracy (0.87) was achieved by the spectral metrics CIRE and NDVI in an SVM model, outperforming models based on the combination of sensor metrics (0.82) or solely LiDAR variables (0.69, MLR). This approach demonstrates their potential for detecting and characterizing morphological changes in up to three levels of mistletoe infestation at individual trees in Mediterranean Scots pine forests, lending support to forest management monitoring.