Inflammation is a biologically resistant response to harmful stimuli, such as infection, damaged cells, toxic chemicals, or tissue injuries. Its purpose is to eradicate pathogenic micro-organisms or irritants and facilitate tissue repair. Prolonged inflammation can result in chronic inflammatory diseases. However, wet-laboratory-based treatments are costly and time-consuming and may have adverse side effects on normal cells. In the past decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction model called AIPs-SnTCN to predict anti-inflammatory peptides accurately. The peptide samples are encoded using word embedding techniques such as skip-gram and attention-based bidirectional encoder representation using a transformer (BERT). The conjoint triad feature (CTF) also collects structure-based cluster profile features. The fused vector of word embedding and sequential features is formed to compensate for the limitations of single encoding methods. Support vector machine-based recursive feature elimination (SVM-RFE) is applied to choose the ranking-based optimal space. The optimized feature space is trained by using an improved self-normalized temporal convolutional network (SnTCN). The AIPs-SnTCN model achieved a predictive accuracy of 95.86% and an AUC of 0.97 by using training samples. In the case of the alternate training data set, our model obtained an accuracy of 92.04% and an AUC of 0.96. The proposed AIPs-SnTCN model outperformed existing models with an ∼19% higher accuracy and an ∼14% higher AUC value. The reliability and efficacy of our AIPs-SnTCN model make it a valuable tool for scientists and may play a beneficial role in pharmaceutical design and research academia.