Introduction: In computational biology, the remote homology detection of the protein sequence is one of the ultimate problems that aims to discover the protein sequences from the database of known protein structures that are evolutionarily associated with a known query protein. Protein homology detection plays a major role in predicting the structures and functions of the protein. Sequence and structure-based comparison allow to detection of the remote homologous. Aim: Several existing computational techniques have been developed to predict remote homology detection in protein sequences. Due to low similarities in protein sequence, the performance of the existing systems is still low. To overcome the drawbacks of the existing systems, this research work proposed a Cascading Maximum Entropy Markov Model (C-MEMM) for remote homology detection. Methodology: The C-MEMM algorithm improved freedom in selecting features to signify the annotations for sequence tagging methods rather than Hidden Markov Model (HMMs). In sequence tagging circumstances, it is valuable to practice domain knowledge to project special-purpose features. The proposed C- MEMM algorithm allows the user to specify lots of correlated, but informative features. Results: Three different organisms such as Xenopus laevis, Bacillus stearothermophilus and Escherichia coli were used for testing the execution of the C- MEMM algorithm with the existing methods. The effectiveness of the proposed C-MEMM algorithm is tested with Coverage and Precision values. Experimental results show that the proposed method effectively improved prediction performance. Conclusion: From the experimental results, it is inferred that the proposed C-MEMM algorithm gives better results than the existing algorithms based on the performance metrics such as coverage rate and precision values for all the organisms.