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We present a python-based program for phenomenological investigations in particle physics using machine learning algorithms, called MLAnalysis. The program is able to convert LHE and LHCO files generated by MadGraph5_aMC@NLO into data sets for machine learning algorithms, which can analyze the information of the events. At present, it contains three machine learning (ML) algorithms: isolation forest (IF) algorithm, nested isolation forest (NIF) algorithm, kmeans anomaly detection (KMAD), and some basic functionality to analyze the kinematic features of a data set. Users can use this program to improve the efficiency of searching for new physics signals. Program Title: MLAnalysis CPC Library link to program files: https://doi.org/10.17632/xnrgv2z76h.1 Developer's repository link: https://github.com/NBAlexis/MLAnalysis Code Ocean capsule: https://codeocean.com/capsule/3663195 Programming language: Python3.8 and above Nature of problem: With the continuous accumulation of experimental data, the research of high energy physics needs to process a large amount of data. ML methods can help us to improve the effect and efficiency of data analysis. Converting the data from experiments or Monte Carlo (MC) simulated events into data sets available for ML has become an important requirement. A program platform is needed for data preparation, as well as the application of various ML algorithms to improve the selection capability of target events and the efficiency of particle identification. Solution method: Supply an event analysis platform that supports ML approaches. The program is able to convert LHE and LHCO files into data sets that can be used for ML algorithms, and apply data preparation. In the data preparation step, the program transforms the raw data into a format that can be used to train and test machine learning algorithms, optimizes the adaptabilities and generalization capabilities of algorithms. The program offers several algorithms, including IF, NIF, and KMAD, which provide NP model independent and standard model effective field theory operator independent methods to optimize event selection strategies. [ABSTRACT FROM AUTHOR]