This paper presents a novel approach for protein sequence-encoded prediction using a swarm-based support vector machine (SVM) optimization method. The proposed method, called w-SVM, combines the chameleon swarm algorithm (CSA) with SVM to effectively predict proteins from amino acid sequences. The features of proteins are extracted using 2DLDA for mutation rate-based amino acids, continuous wavelet transform (CWT) for hydrophilicity-based features, and discrete wavelet transform (DWT) for hydrophobicity-based features. The features are encoded using Bayesian optimization (BO)-based SVM, and the weights of SVM are obtained through parametric tuning of CSA. The experimental analysis is conducted in MATLAB, and the results are compared with state-of-the-art methods such as LSTM, DSSP, and ARIMA. The results show that the proposed approach outperforms these methods in predicting proteins. The main contributions of the proposed approach include data collection based on mutation rate, hydrophilicity, and hydrophobicity features, feature extraction using 2DLDA, CWT, and DWT, and the use of BO-based SVM with CSA for feature encoding and weight optimization. The paper also discusses the advantages and disadvantages of the proposed method compared to existing approaches. The literature survey highlights the limitations of previous methods, such as false positives, false negatives, and low coverage, and the proposed method addresses these issues. The paper concludes that the proposed method provides a more accurate and efficient way to predict proteins.This paper presents a novel approach for protein sequence-encoded prediction using a swarm-based support vector machine (SVM) optimization method. The proposed method, called w-SVM, combines the chameleon swarm algorithm (CSA) with SVM to effectively predict proteins from amino acid sequences. The features of proteins are extracted using 2DLDA for mutation rate-based amino acids, continuous wavelet transform (CWT) for hydrophilicity-based features, and discrete wavelet transform (DWT) for hydrophobicity-based features. The features are encoded using Bayesian optimization (BO)-based SVM, and the weights of SVM are obtained through parametric tuning of CSA. The experimental analysis is conducted in MATLAB, and the results are compared with state-of-the-art methods such as LSTM, DSSP, and ARIMA. The results show that the proposed approach outperforms these methods in predicting proteins. The main contributions of the proposed approach include data collection based on mutation rate, hydrophilicity, and hydrophobicity features, feature extraction using 2DLDA, CWT, and DWT, and the use of BO-based SVM with CSA for feature encoding and weight optimization. The paper also discusses the advantages and disadvantages of the proposed method compared to existing approaches. The literature survey highlights the limitations of previous methods, such as false positives, false negatives, and low coverage, and the proposed method addresses these issues. The paper concludes that the proposed method provides a more accurate and efficient way to predict proteins.