Text-to-Speech (TTS) synthesis using deep learning relies on voice quality. Modern TTS models are advanced, but they need large amount of data. Given the growing computational complexity of these models and the scarcity of large, high-quality datasets, this research focuses on transfer learning, especially on few-shot, low-resource, and customized datasets. In this research, "low-resource" specifically refers to situations where there are limited amounts of training data, such as a small number of audio recordings and corresponding transcriptions for a particular language or dialect. This thesis, is rooted in the pressing need to find TTS models that require less training time, fewer data samples, yet yield high-quality voice output. The research evaluates TTS state-of-the-art model transfer learning capabilities through a thorough technical analysis. It then conducts a hands-on experimental analysis to compare models' performance in a constrained dataset. This study investigates the efficacy of modern TTS systems with transfer learning on specialized datasets and a model that balances training efficiency and synthesis quality. Initial hypotheses suggest that transfer learning could significantly improve TTS models' performance on compact datasets, and an optimal model may exist for such unique conditions. This thesis predicts a rise in transfer learning in TTS as data scarcity increases. In the future, custom TTS applications will favour models optimized for specific datasets over generic, data-intensive ones.