Effort estimation in software development is crucial, since it enables a company to distribute resources efficiently, establish project schedules, and formulate budgets. Conventional approaches to the quantification of software effort occasionally rely on complex models and a detailed pre-processing of data, which makes them very resource-consuming and time-intensive. This research paper examines the feasibility of lazy algorithms as a feasible alternative method of assessing software effort in real life context. The main goal is to improve the estimating process, but maintain accuracy. The use of relaxed algorithms, including case-based reasoning and instance-based learning methods offers a distinct way of predicting software development effort. These strategies utilise real-time data with minimum pre-processing, hence providing an advantageous estimation strategy. Lazy algorithms are the ones that could quickly adjust to emerging projects and enhance the precision of forecasts as the data expands through the use of past cases and the related results. Proposed study findings indicate that it is possible to achieve the same accuracy results with lazy algorithms as with more complex models, and requires less data preprocessing and model complexity. This shows that the lazy algorithms can become a potentially successful way of estimating software effort. Out of two lazy algorithms i.e. K* and IBL empirically evaluated in this research paper, Lazy IBL has shown impressive results for three datasets i.e. NASA, Heiat Heiat and Desharnais out of four used in this research study.