MicroRNAs (miRNAs) are single-stranded, small, non-coding RNAs, that control gene expression at the post transcriptional level through various mechanisms such as translational inhibition, degradation and destabilisation of their target mRNAs. Despite the fact that thousands of miRNAs have been reported in various species, most still remain unknown. Due to this, the identification of new miRNAs is an essential process for analysing miRNA mediated post transcriptional regulation mechanisms. Moreover, many biological approaches suffer from limitations in their capacity to reveal rare miRNAs, and are further restricted to the state of the organism under examination. Such limitations have resulted in the construction of sophisticated computational tools for identification of possible miRNAs in silico. However, these programs suffer from low sensitivity and/or accuracy and as a result they do not provide enough confidence for validating all their predictions experimentally. In this study, the aim is overcoming these challenges by creating a new and adaptable machine learning based method to predict potential miRNAs in any given sequence. The efficiency of proposed method is shown by comparison with available tools on various data sets. By using this approach, miRNAs from the genomes of various organisms like human (Homo sapiens), fly (Drosophila melanogaster) and tomato (Solanum lycopersicum) are identified. Moreover, networks between the possible miRNAs of virus and human genes as well as the communications among nuclear and organelle genomes of Solanum lycopersicum through miRNAs are investigated.