The identification of anticancer peptides (ACPs) has emerged as a critical research area due to their potential to revolutionize cancer treatment, offering more targeted and less toxic alternatives to traditional therapies. Most existing deep-learning models mainly focus on primary sequence information and basic physicochemical properties. They frequently neglect long-range dependencies and positional relationships within peptide sequences. In this study, we introduce ACP-TransLSTM, a novel framework for anticancer peptide prediction that employs a comprehensive feature extraction strategy, integrating amino acid composition, structural and physicochemical properties. By combining handcrafted descriptors with Transformer-based representations, the model better captures the diverse characteristics of peptides. Its architecture combines Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks, Bi-LSTM can process sequential data bidirectionally, integrating information from both past and future in the peptide sequence. We tested ACP-TransLSTM on six public datasets and the experimental results demonstrate that ACP-TransLSTM exhibits strong robustness and performs more competitive than compared methods, particularly in terms of AUC and ACC. For example, in terms of the average ACC and AUC across the six datasets, ACP-TransLSTM achieves 0.907 and 0.93 respectively, outperforming the best-performing compared method by at least 2.7% in average ACC and 1.6% in average AUC. The source code for ACP-TransLSTM is available at https://github.com/qdu-bioinfo/ACP-TransLSTM .