<![CDATA[Abstract: The advancement of Artificial Intelligence (AI) has driven the widespread adoption of Machine Learning (ML) across various sectors. This study aims to analyze the development trends, implementation sectors, dominant algorithms, key challenges, and future research directions of ML during the 2020–2026 period. Utilizing a Systematic Literature Review (SLR) based on PRISMA 2020 and Kitchenham Guidelines, 120 peer-reviewed articles from six international academic databases were systematically evaluated. The findings indicate a significant increase in ML publications post-Generative AI era, with the highest adoption rates observed in the healthcare, finance, and manufacturing sectors. Algorithms such as Random Forest, XGBoost, CNN, and LSTM dominate for prediction and classification tasks. Despite its benefits, the primary challenges of ML deployment involve data quality, privacy, algorithmic bias, and model interpretability. This study highlights a shifting research paradigm toward transparent and human-centered AI, outlining future agendas in Explainable AI (XAI), Federated Learning, and Green AI. Keywords: Artificial Intelligence; Explainable AI; Federated Learning; Generative AI; Machine Learning. Abstrak: Perkembangan Artificial Intelligence (AI) mendorong pemanfaatan Machine Learning (ML) secara luas di berbagai sektor lintas disiplin. Penelitian ini bertujuan menganalisis tren perkembangan, sektor implementasi, algoritma dominan, tantangan utama, serta arah penelitian masa depan terkait pemanfaatan ML selama periode 2020–2026. Metode yang digunakan adalah Systematic Literature Review (SLR) berbasis pedoman PRISMA 2020 dan Kitchenham Guidelines terhadap 120 artikel ilmiah terpilih dari enam database akademik internasional. Hasil tinjauan menunjukkan adanya peningkatan signifikan publikasi ML pasca-era Generative AI, dengan adopsi tertinggi pada sektor kesehatan, keuangan, dan manufaktur. Algoritma berbasis data tabular (seperti Random Forest dan XGBoost) serta data citra/deret waktu (seperti CNN dan LSTM) menjadi yang paling dominan digunakan untuk kebutuhan prediksi dan klasifikasi. Meskipun menawarkan efisiensi tinggi, tantangan utama implementasi ML berpusat pada kualitas data, privasi, bias algoritma, dan interpretabilitas model. Penelitian ini merumuskan agenda riset masa depan yang mulai bergeser ke arah pengembangan AI yang transparan dan berpusat pada manusia, seperti Explainable AI (XAI), Federated Learning, dan Green AI. Kata Kunci: Artificial Intelligence; Explainable AI; Federated Learning; Generative AI; Machine Learning.]]>
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