Holy Basil (Ocimum sanctum), commonly known as Tulsi, holds a sacred and medicinal prominence in traditional healing systems, particularly in Ayurveda. Basil is used to treat respiratory disorders, fever, diabetes, stress, and various infections due to its antimicrobial, anti- inflammatory and immunomodulatory effects. These plants are susceptible to a range of phytopathogenic threats, including fungal infections, bacterial blights, viral diseases, and arthropod infestations. These diseases weaken the plant, reducing its bioactive compounds and lead to complete crop loss. Early detection of these diseases is pivotal to reduce extensive damage. The study investigated the performance of different machine learning as well as deep learning techniques like MobileNetV2 and EfficientNetB0 with SVM, KNN and Random Forest, Transfer learning using EfficientNetB0, DensetNet121, ResNet50 etc. on a dataset of our own with 803 images which are divided into 5 classes namely Leaf spot, Downy mildew, Fusarium wilt, Fungal and Healthy which are identified as a disease which affects basil leaves. Here, we presented a hybrid model of pre-trained CNN model EfficientNetB0 with SVM performing best amongst others. EfficentNetB0 is used to extract feature implicitly unlike ML algorithms whereas SVM is used to classify images into different classes. By data augmentation, we make the dataset balanced enhancing its overall performance compared to traditional CNN. Our hybrid model i.e. EfficientNetB0 + SVM has performed reasonably well with the testing accuracy of 98.86% after fine tuning it by unfreezing some of its layers and adding a dropout and a dense layer to the EfficientNetB0 model. This result is validated using cross validation with a CV score of 96.11.