Citrus plants play a pivotal role in the global agriculture landscape, making significant contributions to the economy. However, the agricultural sector faces challenges posed by the escalating prevalence of diseases affecting citrus plants. Early and precise identification of these diseases is imperative for implementing timely interventions and minimizing crop losses. This research endeavors to assess and compare diverse machine learning classifiers for effectively classifying citrus plant diseases. The objective is to evaluate their performance, accuracy, and appropriateness in addressing the intricate challenge of identifying and categorizing diseases that impact citrus plants. The research leverages the PlantifyDr Dataset from Kaggle, concentrating on categories such as Citrus Black Spot, Citrus Healthy, Citrus Canker, and Citrus Greening. Various machine learning classifiers, including Random Forest, Support Vector Machine, Decision Tree, K-Nearest Neighbour, Logistic Regression, and Naïve Bayes, are deployed for classification. The assessment includes evaluating the accuracy, precision, recall, and F1 score to comprehensively analyze the performance of these classifiers. Remarkably, the Random Forest model stands out as highly effective, achieving an accuracy of 95.42%. The findings provide crucial insights, advocating for a paradigm shift towards innovative and automated solutions to alleviate the impact of plant diseases on global food production.