There are a number of websites that fulfil a range of roles in the modern digital world. These functions include the transmission of information, the construction of connectivity, and other functions. The identification of dangerous API calls and photos was the primary focus of this research, with the ultimate goal of discovering the precise risks that were addressed by the files. The investigation was carried out with the intention of completing the inquiry. The examination of application programming interface (API) calls and graphics makes it possible to discriminate between programmes that are hazardous and those that are not harmful. In this particular investigation, classification analysis was employed for the objective of classifying malware based on textual information. The classification models that were utilised consisted of five unique models, including support vector machine, naïve bayes, and random forest, KNN, and decision tree. These models were selected because of the numerous benefits that machine learning and deep learning techniques offer, as well as the widespread usage of these approaches. During the course of the study project, the Convolutional Neural Networks technique was employed for the goal of categorising the pictures that were included in the Malimg dataset. Both the Malimg dataset and the APIMDS dataset were utilised in order to complete the evaluation of this approach. The results of the trials reveal that the suggested technique is capable of efficiently categorising malware with a high level of accuracy, which is superior to the methods.