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Ransomware is one of the most significant cybersecurity threats facing the world. In this research we designed and constructed a custom cybersecurity AI dataset for ransomware detection. We then evaluated the dataset using different machine learning models. The dataset was constructed using Cuckoo Sandbox where raw ransomware samples were analyzed to extract key features such as API calls, DLL usage, file operations, network activity, process creation and registry changes. These were then carefully labeled as either ransomware or benign. For evaluation purposes, the custom cybersecurity AI dataset was utilized to train and test various machine learning models. The dataset was split into 80% for training and 20% for testing. Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), and XGBoost models were used to evaluate the resulting custom Cybersecurity AI Dataset. We obtained higher results of accuracy, precision, recall, and F1 scores evaluation metrics. Moreover, our results demonstrate the robustness of a combination of well-designed custom Cybersecurity AI Datasets and machine learning techniques in enhancing ransomware detection mechanisms as well as providing a framework for future cybersecurity applications