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In this study, we develop Neural Machine Translation (NMT) andTransformer-based transfer learning models for English-to-Igbo translation - alow-resource African language spoken by over 40 million people across Nigeriaand West Africa. Our models are trained on a curated and benchmarked datasetcompiled from Bible corpora, local news, Wikipedia articles, and Common Crawl,all verified by native language experts. We leverage Recurrent Neural Network(RNN) architectures, including Long Short-Term Memory (LSTM) and GatedRecurrent Units (GRU), enhanced with attention mechanisms to improvetranslation accuracy. To further enhance performance, we apply transferlearning using MarianNMT pre-trained models within the SimpleTransformersframework. Our RNN-based system achieves competitive results, closely matchingexisting English-Igbo benchmarks. With transfer learning, we observe aperformance gain of +4.83 BLEU points, reaching an estimated translationaccuracy of 70%. These findings highlight the effectiveness of combining RNNswith transfer learning to address the performance gap in low-resource languagetranslation tasks.

Low-Resource Neural Machine Translation Using Recurrent Neural Networks and Transfer Learning: A Case Study on English-to-Igbo