In order to maintain public health, food security, and livestock productivity, animal health monitoring is essential. Traditional illness detection techniques frequently depend on laboratory testing and manual observation, which are expensive, time-consuming, and prone to causing delays in early intervention. Humans can easily communicate and share a problem; however, when it comes to animals, they cannot communicate or share their discomfort. Our objective in this paper is to detect and predict various health risks in different animals with high accuracy, thereby promoting a healthy lifestyle and minimizing the risk of death. Additionally, our model facilitates early intervention, contributing to reduced mortality rates. We used machine learning algorithms like random forest, SVM, LoGR, DT, Naive Bayes, and KNN, which were trained and evaluated for baseline performance. Apart from this, we also used the DL models ANN and CNN to capture complex nonlinear patterns and high-dimensional feature interactions. The performance metrics were evaluated to determine which model performed well. The proposed ML and DL models achieved high classification performance, with the random forest and CNN models outperforming the others. We measured the accuracy of the CNN model to be 94.8%, with a precision of 93.2%, recall of 95.1%, and F1 score of 94.1%. This means that the CNN has one of the strongest predictive capabilities for identifying early health risk across multiple animal species. We also found that the AUC-ROC score was 0.96, which indicates that our model perfectly classifies healthy and diseased cases for early diagnosis and intervention in animal healthcare.