The increase in chemical diversity has increased the need to adjudicate the toxicity of different chemical compounds raising the burden on the demand of animal testing. The toxicity evaluation requires, time consuming and expensive task leading to the deprivation of the methods used for screening chemicals pointing towards the need to develop more efficient toxicity assessment systems. Computational approaches have reduced the time as well as cost for evaluating the toxicity and kinetic behaviour of any chemical. The availability of a large amount of data and the intense need of turning that data into useful information has attracted the attention towards data mining. Machine Learning, one of the powerful in silico data mining techniques has evolved as the most efficient and powerful tool for exploring new insights on combinatorial relationships among various experimental data generated. The article accounts on some sophisticated machine learning algorithms like Artificial Neural Networks, Support Vector Machine, k-mean clustering and Self Organizing Maps with some of the available tools used for classification, sorting and toxicological evaluation of data, clarifying, how data mining and machine learning interact cooperatively to facilitate knowledge discovery. Addressing the association of some freely available expert systems, we briefly outline some real world applications to consider the crucial role of data set partitioning.