SmartCompress AI: An Entropy-Aware Hybrid Machine Learning Framework for Intelligent Lossless Compression Algorithm Selection

Organisations store ever larger volumes of logs, records, source code and backups, yet the choice of compression algorithm for each file is usually left to habit, with one archiver applied to everything.This paper presents SmartCompress AI, a web-based storage optimisation framework that inspects a file before compressing it and recommends the most suitable lossless engine for the user's goal.The system measures Shannon entropy over a streaming 64 KB window, extracts file size, category and extension, and passes these features together with a user preference (fastest, balanced or maximum compression) to a Random Forest classifier.A rule-based engine guards the classifier for pre-compressed and near-random data and acts as a fallback whenever the model is unavailable.Six engines are supported: ZIP, GZIP, BZIP2, LZMA, 7Z and PDF stream re-deflation.On a 17-file, 4.01 MB benchmark corpus, LZMA achieved the highest aggregate saving of 92.28% but needed 567.6 ms in total, about 14 times longer than ZIP (40.8 ms, 90.19% saving).The classifier reached 61.5% held-out accuracy, yet its wrong choices cost only 0.13 percentage points of saving on average, because the candidate engines are close for most compressible data.Every operation is recorded in a SQLite ledger and can be exported as a PDF audit report, and all 34 automated tests pass.The framework demonstrates that a lightweight, explainable recommender can make compression choices transparent and resource-aware, supporting sustainable data storage.