Climate change has emerged as a major global challenge, significantly impacting small-scale businesses and startups, particularly in sectors such as agriculture, manufacturing, energy, and retail. These businesses often lack the financial and infrastructural resilience to cope with climate-induced disruptions, making them highly vulnerable to extreme weather events, fluctuating resource availability, and changing consumer behaviors. However, the integration of Machine Learning (ML) and Deep Learning (DL) algorithms presents new opportunities to mitigate risks and enhance adaptive strategies. This paper provides a comprehensive review of how ML and DL models, including Random Forest, Support Vector Machines (SVM), Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNNs), are being used to forecast climate-induced risks, optimize supply chains, reduce operational inefficiencies, and ensure business continuity. Additionally, real-world case studies demonstrate how AI-driven climate adaptation strategies have helped businesses improve decision-making and sustainability.

Keywords: Climate Change, Small-Scale Businesses, Startups, Machine Learning, Deep Learning, Climate Resilience, AI-driven Sustainability, Business Risk Mitigation, Artificial Intelligence, Supply Chain Optimization, Sustainability, Business Continuity, Climate Risk Management.

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Source of Funding:

This research did not benefit from grant from any non-profit, public or commercial funding agency.

Competing Interests Statement:

The authors have declared that no competing financial, professional or personal interests exist.

Consent for publication:

The authors wrote and edited the manuscript and consented to the publication of this research work.

Availability of Data and Material:

Not applicable for this study.