Digital media serves as the method of choice for advanced cybercriminals to disguise their illegal data because it makes it extremely challenging for forensic investigators to trace. The conducted research introduces StegAnalysis Suite as an application to support total forensic analysis of data concealed via steganographic techniques. The detection algorithms running in the tool unite F5 with Least Significant Bit (LSB) analysis and Chi-Square statistics and utilizes machine learning approaches through Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). The tool works with JPEG and PNG images and MP3 contents and provides well-designed interfaces with complete forensic reporting functions to help investigators perform more efficiently. The suite achieves remarkable detection performance through performance tests conducted using synthetic and genuine datasets which show its ability to generate accurate findings in hidden data discovery. Steganography-based cybercrime has become easier to combat through the StegAnalysis Suite because forensic technology received better capabilities from advancements in digital methods.