This study examines avian influenza outbreak identification using advanced machine learning models. The dataset includes geographical coordinates, species information, and temporal data. Initial preprocessing involved converting columns to numerical types and removing outliers with the Isolation Forest algorithm, isolating 5% of the data as outliers. Data cleaning ensured dataset integrity. Feature correlations were analyzed, focusing on those linked to H5 highly pathogenic avian influenza (HPAI). Machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Gradient Boosting, were used to predict the target variable. Performance was evaluated using ROC curve and AUC metrics, with the Random Forest model showing the highest AUC score. Deep learning models, specifically a neural network and a convolutional neural network (CNN), were implemented to enhance predictive accuracy. The CNN outperformed traditional machine learning models, demonstrating the potential of deep learning in epidemiological predictions. The study underscores the efficacy of these techniques in predicting avian influenza outbreaks, highlighting the importance of advanced analytical methods in public health predictive modeling.