Guava is an essential fruit for human livelihood, providing cures for many infections and health issues. Guava leaf is also used for blood sugar regulation, promoting digestive well-being, and exhibiting antimicrobial properties. While guava has numerous uses, the occurrence of diseases could pose problems for farmers. To detect diseases in guava, the YOLOv8 model is utilized, benefiting farmers in guava crop cultivation. Various pathogens, bacteria, fungi, and infections can impact the yield and quality of guava crops. The YOLOv8 model, trained with 640px-sized images, achieves approximately 96.6% accuracy, with box loss at 1.054, class loss at 0.7533, and domain flow loss at 1.916 after 150 epochs of training. The reduced loss functions result in increased precision and recall percentages. All classes show 0.983 mAP@0.5. The Guava leaf disease detection system empowers farmers with timely and accurate disease identification, allowing for targeted interventions and minimizing crop losses. This advanced technology contributes significantly to the sustainability and success of guava crop cultivation. With the implementation of the YOLOv8 model, guava farmers can now proactively address disease outbreaks, leading to improved overall crop health and increased yields. By harnessing the power of image analysis and advanced deep learning, the Guava leaf disease detection system revolutionizes the way guava crops are managed, ensuring a resilient and thriving agricultural sector.