Genomic islands(GIs) are clusters of genes that are acquired during the Horizontal Gene Transfer process(HGT) by bacterial genomes. These islands play a crucial role in the evolution of bacteria by helping them adapt to changing environments. The detection of GIs is therefore an important problem in medical and environmental research. There have been many previous studies on computationally identifying GIs, but most of the studies rely on either closely related genomes or annotated nucleotide sequences with predictions based on a fixed set of known features. Previous research on unannotated sequences has not been able to reach a good accuracy due to the lack of information taken into account while prediction and lack of GI boundary detection method. In this thesis, I present a machine learning-based framework called TreasureIsland, that uses an unsupervised representation of DNA sequences to predict GI. I propose to improve the boundary detection problem of GI by using a boundary fine-tuning method to attain better precision. I evaluate the efficiency of my framework by using a reference dataset obtained by the comparative genomics method and from the literature. The evaluations show that this framework was able to achieve a high recall and accuracy when compared to other GI predictors.