Introduction: It was a century ago, whether used separately or in combination with other medicines, that radiotherapy evolved into a successful cancer treatment. In traditional radiation therapy, a dose volume histogram (DVH) is utilized for quantitative analysis of the treatment plan after the adoption of the treatment planning system. An isodose distribution is also used for qualitative analysis and evaluation of the treatment plan during this phase. The right treatment plan is assessed using physical and radiobiological models by in-house software developed for radiation oncologists. Materials and Methods: The first process was the OSCAR (Object Scoring with Coloured Area of Regret) treatment planning system, developed by Theratronics International, Kanata, Canada in 1991, which was one of the numerous plan evaluation software programs that were established in the field of radiation medicine to analyse and regularize the dose distribution. Many plan evaluation programs have been created over the developing years, with the commercial software used primarily and the primary inclusion MATLAB, and very few have been created using Microsoft Visual Basic, C++, and Java. As a substitute for MATLAB, Python is utilized because it is freely available and has no commercial value. Results: This study’s primary objective is to personalize the radiobiological effects to predict each patient by using the DVH data, which are now accessible, to improve the overall performance of the prior model. Several programming languages were initially investigated to check the portability and quick program execution to illustrate the novel ideas. It was discovered that Python was adequate for this research even if it has no economic value compared to other languages. The software receives the DVH data in text format as an input, and for convenience, it displays the output using a variety of Python widgets. Discussion: The major radiobiological parameter values, TD50/5, slope parameter (m), and volume parameter (n), are used to calculate the tumor control probability (TCP) and NTCP values of numerous targets and oars from their respective DVH statistics using Python software. The evaluation of the physical indices of the treatment plans, the AAPM, RTOG, and QUANTEC protocols were used in a clinical analysis for the execution of treatment plans. Conclusion: The problem in the previous plan evaluation tool was fixed by the custom-made PYTHON program used for this research investigation, which also added clinical and radiobiological understanding of the treatment plan. The software produces a report using Microsoft Excel for the comprehensive radiobiological and dosimetric plan evaluation study for cancer patients.