Technical debt (TD) is a by-product of short-term optimisation that results in long-term disadvantages. Because every system gets more complicated while it is evolving, technical debt can emerge naturally. The impact of technical debt is great on the financial cost of development, management, and deployment, it also has an impact on the time needed to maintain the project. As technical debt affects all parts of a development cycle for any project, it is believed that it is a major aspect of measuring the long-term quality of a software project. It is still not clear what aspect of a project impact and build upon the existing measure of technical debt. Hence this experiment, the ultimate task is to try and estimate the generalisation error in predicting technical debt using software metrics, and adaptive learning methodology. As software metrics are considered to be absolute regardless of how they are estimated. The software metrics were compiled from an established data set; Qualitas.classCorpus, and the notions of technical debt were collected from three different Staticanalysis tools; SonarQube, Codiga, and CodeClimate.The adaptive learning methodology uses multiple parameters and multiple machine learning models, to remove any form of bias regarding the estimation. The outcome suggests that it is not feasible to predict technical debt for small-sized projects using software-level metrics for now, but for bigger projects, it can be a good idea to have a rough estimation in terms of the number of hours needed to maintain the project.