In today’s day of Modern era when the data handling objectives are getting bigger and bigger with respect to volume, learning and inferring knowledge from complex data becomes the utmost problem. Almost all of the real-world information are maintained under a relational fashion holding multiple relations unlike orthodox approaches containing single relational as a whole. Moreover several fields viz. biological informatics, microbiology, chemical computations needed some more dependable and expressive approach which can provide more sophisticated results with faster speed. Hence in context with multi-relational data mining in which data is directly retrieved from different records without dumping into single table, we have described a novel approach of improved Multi-Relational Decision Tree Learning Algorithm based on the implementations. In this paper provided a comparative study of the aforementioned approach in which we have taken certain results from the literature review. Experiments mainly includes results from widely used datasets viz. Mutagenesis database which demonstrates that Multi-Relational Decision Tree Learning Algorithm provides a promising alternative from previous conventional approaches such as Progol, FOIL, and Tilde.