Abstract: Volunteering is an important and beneficial phenomenon in South Africa, it has a significant contribution to both our economy and sustainable developments. As a developing country we are still faced with the issues of poverty, unemployment, teenage pregnancy, and limited service delivery. As a result, there is a gap between the social need services demand and the government supply. This gap inspires both the government and South Africans to come up with initiatives to try to fill the gap through volunteering. Volunteering is any form of activities, which gives out of the free will to other people or organization in need. Due to a high increase in volunteering activities in South Africa, Forgood has developed an online platform to easily connect passionate people with causes in need. Even though the platform is freely available for everyone, the insights from the data show that the platform is not fully utilized. This paper seeks to use machine learning and data analytics to uncover hidden patterns and to develop tools that can be used to improve the volunteering transaction success in the platform. These will be done using the most popular and well-known algorithms like logistic regression, Random forest, and K-means algorithm. The logistic regression model is used to analyse the relationship between the need response and the predictor variables, and predict the probability of a need getting a response given feature vector X. The K-means clustering algorithm is used to cluster the volunteers into two groups based on their volunteering behaviors. Lastly, the random forest model is used to predict the volunteer's status based on the predictors.