Machine Learning (ML) technologies have become essential tools, transforming industries and unlocking incredible potential in various fields. ML is now widely used for data-driven decision-making and predictive analytics across fields like healthcare, finance, transportation, and more. However, building and implementing ML models can be complex and time-consuming, often requiring programming proficiency and data science skills. Despite significant progress in ML, non-experts often struggle with selecting algorithms, optimizing models, and deploying ML solutions. This paper conducts a systematic literature review to explore challenges in the area of machine learning based on multiple categories involving features engineering and data extraction, learning model structure and activities, learning-based analysis and visualization, analysis algorithms in data-based systems, machine learning algorithms and systems development, and declarative ML-based prediction. Addressing these challenges underlines the importance of following AutoML and Declarative ML strategies in simplifying the ML process.