<p>This study presents a comparative analysis of machine learning algorithms for classifying sea animal images using Orange Data Mining. The dataset, consisting of labeled images categorized into three classes—squid, statfish, and whale—was sourced from an open-access repository and processed using supervised learning workflows. Various models, including Neural Networks, Support Vector Machines (SVM), Random Forests, k-Nearest Neighbors (kNN), Logistic Regression, Naïve Bayes, Gradient Boosting, and AdaBoost, were evaluated using 10-fold cross-validation. Performance was assessed across multiple metrics: Area Under the Curve (AUC), Classification Accuracy (CA), F1-score, Precision, Recall, and LogLoss. The Neural Network model yielded the best overall performance with an AUC of 0.990 and a classification accuracy of 93.2%. SVM and Logistic Regression closely followed, outperforming other traditional and ensemble methods. Confusion matrix analysis further supported these findings, demonstrating low misclassification rates for Neural Networks. ROC curve evaluations for individual classes confirmed the robustness of top-performing models. The findings validate the effectiveness of low-code platforms like Orange in streamlining image classification pipelines for ecological and biological image datasets. Unlike prior studies that focus on a single deep learning model or custom code-centric pipelines, this work provides a comparative benchmark of nine supervised machine learning algorithms implemented in a low-code environment (Orange). This novelty supports non-programmer practitioners in rapidly deploying accurate sea-animal image classifiers while preserving interpretability and scalability. The findings highlight the potential of low-code tools as an accessible pathway for ecological image analysis and marine species monitoring. This study provides valuable insights for researchers aiming to deploy interpretable and scalable machine learning solutions in marine biology and related domains.</p>