The process of identifying the correct sense of a given word in a particular sentence is called Word Sense Disambiguation (WSD). It is complex problem because it involves drawing knowledge from various sources. Significant amount of effort has been put into resolving this problem in machine learning since its inception but the toil is still ongoing. Many techniques were used in WSD and implemented on different corpora for almost all languages. In this paper, WSD algorithms were classified to three categories as Knowledge-based, supervised and unsupervised techniques. Each category will be studied in details with explanation of almost all the algorithms in each category. Hence work examples for each method were taken with the used language, the used corpora and other factors. The benefits and drawback of each method were recorded. Some of these techniques have limitations in some situations, therefore this work will helps the researchers in the field of natural language processing to select the suitable algorithms to solve their particular problem in WSD. The novelty of the work can be seen in the comparison of the used works and the used algorithms. From this work, it was concluded that (i) some methods give high accuracy for language but low for other, (ii) the size of the used data set affects the performance of the used algorithm, (iii) some of these approaches can be run fastly but with limitation of the accuracy and (iv) most of these approaches are implemented for many languages successfully.