Computer models for therapeutic efficiency and toxicity have been a trendy topic in recent years. Computational models help speed up learning opportunities for drug development and improve safety and efficiency. In this particular investigation, our objective will be to create sophisticated computer models for effectively forecasting the efficacy and toxicity of pharmaceuticals. We will employ machine learning, deep learning, and statistics innovation to analyze massive pharmacological efficacy and toxicity data sets. The study will commence with the acquisition of data pertaining to the drug's efficacy and toxicity from diverse sources, including publicly accessible clinical trials and inclusive databases. After that, we will preprocess the data by removing any outliers and values that were missing using sophisticated statistical techniques. The last step will be to construct prediction models for pharmacological effectiveness and toxicity technological progress. Furthermore, a thorough feature selection and analysis will be conducted to determine the primary attributes that significantly impact the efficacy and toxicity of the medication. Furthermore, a comparative analysis will be conducted between our computational models and existing models, revealing the accuracy and predictive power of our models. In summary, our study may demonstrate that advanced computational models possess the ability to precisely predict the efficacy and adverse effects of pharmaceuticals. In addition to enhancing therapeutic effectiveness and safety, our models may shorten the amount of time and money needed for the development of new medications with efficient use of resources, as well as lower related development expenses.