Effective project management relies heavily on the integration of software cost estimation, allowing organizations to efficiently plan, execute, and oversee software development initiatives. This process not only improves decision-making but also enhances capabilities in risk management and facilitates transparent communication with stakeholders. In the end, these contributions collectively play a pivotal role in ensuring the success of software projects. In this work, bagging ensemble learning approach has been used for prediction of software cost estimation from the historical data. This proposed approach is efficient due to its capacity to improve model performance by amalgamating diverse predictions. It achieves this by training multiple base models on distinct bootstrap samples derived from the training data, effectively diminishing overfitting and elevating prediction stability. This ensemble strategy plays a pivotal role in alleviating the influence of outliers and noise within the dataset, culminating in the development of a model that is both robust and dependable.