The profound advancements witnessed in artificial intelligence algorithms, particularly within the domain of machine learning, have led to considerable progress. A hallmark of machine learning algorithms is their capacity to process immense quantities of data and accurately forecast outcomes for tasks such as image recognition and classification. The maritime industry, a vital contributor to the expansion of global trade activities, contends with the formidable challenge of detecting and controlling the multitude of vessels traversing the oceans. Despite satellite images offering an extensive reservoir of oceanic data, a comprehensive appraisal of machine learning algorithm performance in tackling the critical issue of ship identification and detection remains largely absent. This investigation leverages machine learning algorithms from the scikit-learn library to discern and locate ships within the publicly available Kaggle dataset. The results show that using SVM gives the highest result with 80.41%, followed by Logistic regression with 80.34% and the lowest is decision tree with 75.25%. We also use a deep learning algorithm called CNN to compare with algorithms in the sklearn library, the accuracy of CNN is 87.12%. Grid Search optimization method is used for SVM model and Hyperband for CNN model. The reliability result after applying the optimized parameter space of the SVM model is 89% and CNN is 94.44%.