The thesis develops a predictive model for effort estimation in M&A advisory projects, a field where staffing and pricing decisions still rely mainly on managerial intuition. Based on a dataset of 30 completed transactions from an investment banking boutique, structured indicators of complexity and risk are constructed and combined with an expert quick estimate. Among the models tested, the specification integrating structured variables and expert judgment provides the best accuracy, offering a decision-support tool for staffing allocation and capacity planning in investment banking.