Abstract: Exploring and explaining the knowledge hidden in the biomolecular database has become the grand challenge for bioinformatics in the post genome era. An efficient and inexpensive approach is required to solve problems in molecular biology;machine learning which is an automatic and intelligent learning technique may help to a-chieve this role. KDD,ANNs, Decision Trees, BBNs, GAs, HMMs, Clustering, ILP, SVM are introduced in the context of their application in bioinformatics, to experimental biologists and bioinformaticians in this paper. These approaches help to accelerate several major researches (biomolecular structure prediction, gene finding, genomics and proteomics).