This study presents a comparative analysis of multiple machine learning (ML) models include Decision Tree, K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, and Gradient Boosting applied to real-world forest health data. Comprehensive preprocessing techniques such as normalization and standardization were applied, and models were evaluated using key performance indicators including accuracy, precision, sensitivity, F1-score, and specificity. Initial results showed that ensemble models like Random Forest and Gradient Boosting provided the highest accuracy among the models tested. However, after hyper parameter tuning, SVM emerged as the most effective model with near-perfect scores across all metrics, achieving an accuracy of 99.78%. The study highlights the significance of parameter optimization in improving model performance and reinforces the potential of machine learning in ecological monitoring. These findings suggest that ML models, especially ensemble and kernel-based techniques, can provide scalable, accurate, and timely solutions for forest health assessment and decision-making in conservation efforts.