The economically valuable fruit crop guava (Psidium guajava) is susceptible to a number of illnesses that can significantly reduce productivity and quality. In order to improve early detection and intervention, this research study presents a comparative comparison of IT-enabled strategies for guava disease detection. Visual inspection and symptom recognition are traditional procedures that have poor objectivity and accuracy. Modern technologies like image processing and machine learning methods have become more common to overcome these constraints. The effectiveness of several methods for identifying diseases such anthracnose, powdery mildew, and bacterial blight in guava plants is thoroughly reviewed and compared in this study. Using both primary and secondary data, it is possible to detect large-scale changes in orchards that contribute to disease. Additionally, improvements in imaging methods like thermal and hyperspectral imaging offer high-resolution spatial data that can help with precise illness classification. Automated decision support systems for farmers and agricultural practitioners have been developed as a result of machine learning algorithms that display promise classification accuracy when trained on large datasets of spectral and image data. In terms of accuracy, scalability, cost-effectiveness, and applicability in various agricultural settings, the comparison analysis highlights the advantages and disadvantages of each approach. The study emphasizes the necessity of a holistic strategy that incorporates numerous strategies to offer thorough disease assessment and management. In conclusion, guava disease diagnosis could be revolutionized by the incorporation of IT-enabled methodologies. With its insights into prospective paths for future research and development, this study helps us comprehend the current state of guava disease detection systems. IT-related technologies have the ability to guarantee sustainable guava farming and food security by improving disease surveillance and management.