Understanding and predicting the effects of cellular perturbations using single-cell sequencing technology remains a critical and challenging problem in biotechnology. In this work, we introduce CycSeq, a deep learning framework that leverages cyclic data generation and recent advances in neural architectures to predict single-cell responses under specified perturbations across multiple cell lines, while also generating the corresponding single-cell expression profiles. Specifically, CycSeq addresses the challenge of learning heterogeneous perturbation responses from unpaired single-cell gene expression data by generating pseudo-pairs through cyclic data generation. Experimental results demonstrate that CycSeq outperforms existing methods in perturbation prediction tasks, as evaluated using computational metrics such as R-squared and MAE. Furthermore, CycSeq employs a unified architecture that integrates information from multiple cell lines, enabling robust predictions even for long-tail cell lines with limited training data. The source code is publicly available at https://github.com/yczju/cycseq.