  In software engineering, software maintenance is the process of correction,
updating, and improvement of software products after handed over to the
customer. Through offshore software maintenance outsourcing clients can get
advantages like reduce cost, save time, and improve quality. In most cases, the
OSMO vendor generates considerable revenue. However, the selection of an
appropriate proposal among multiple clients is one of the critical problems for
OSMO vendors. The purpose of this paper is to suggest an effective machine
learning technique that can be used by OSMO vendors to assess or predict the
OSMO client proposal. The dataset is generated through a survey of OSMO vendors
working in a developing country. The results showed that supervised
learning-based classifiers like Na\"ive Bayesian, SMO, Logistics apprehended
69.75, 81.81, and 87.27 percent testing accuracy respectively. This study
concludes that supervised learning is the most suitable technique to predict
the OSMO client's proposal.
