This paper presents CrioleSet, a new parallel corpus designed to facilitate neural machine translation (NMT) for Cape Verdean Creole (CVC), a low-resource language spoken by the majority of Cape Verdeans. Comprising over 6,000 translation pairs in English, Portuguese, French, and CVC, the dataset addresses the scarcity of annotated resources for CVC, which is further challenged by dialectal variation across the archipelago. We trained and evaluated three neural network architectures LSTM, GRU with gated attention (GAtt), and Transformer—base to perform English-CVC translation tasks. Experimental results demonstrate that the Transformer-base models significantly outperforms the others, achieving the highest BLEU and METEOR scores and the lowest TER, reflecting better translation quality and robustness. This confirms that attention-based architectures can effectively handle low-resource translation, even with relatively modest datasets. The work underscores the potential of focused data curation and deep learning to advance NLP resources for underrepresented languages. Future work includes expanding the dataset to encompass more dialects and refining models for broader NLP tasks in CVC, thereby contributing to linguistic inclusivity and cross-cultural communication.