The rising prevalence of Phytophthora diseases in forests highlights the need for rapid, non-invasive detection methods. Early-stage root infections are difficult to detect due to the absence of visible above-ground symptoms, while current diagnostics remain slow and invasive. This study investigated whether hyperspectral leaf reflectance could detect root rot caused by Phytophthora alticola in Eucalyptus benthamii. Nineteen commercially planted families were inoculated, and leaf spectra were collected using an ASD FieldSpec 4 sensor. A machine learning pipeline was developed to identify diagnostic spectral signals. Key wavelengths were identified using permutation importance, a genetic algorithm, and self-attention network (SAN) scores. Spectral signals linked to root rot revealed that infection was correlated with leaf pigment accumulation and moisture stress. Three algorithms, random forest (RF), support vector machine (SVM), and SAN, were trained on hyperspectral data to predict P. alticola infection. The SAN achieved 97 % accuracy on a reduced dataset, which included the diagnostic wavelengths from the feature selection step, surpassing the RF (96 %) and SVM (94 %) models. This study demonstrates hyperspectral sensing as an effective tool for detecting Phytophthora root rot using spectra from the foliage and highlights the application of advanced machine learning techniques for plant disease classification.